Update rust.yml

* fix: remove armv7 rpi4 uses aarch64

* fix: remove 0.0.0 as it is causing failures

.github/workflows/pypi.yml

@@ -168,7 +168,7 @@ jobs:
           name: wheels
           path: dist
 
-  # TODO: There's a problem with the maturin-action toolchain for arm arch leading to failed builds
+  #  There's a problem with the maturin-action toolchain for arm arch leading to failed builds
   # linux-cross:
   #   runs-on: ubuntu-latest
   #   strategy:
@@ -283,8 +283,6 @@ jobs:
         platform:
           - target: aarch64-unknown-linux-musl
             arch: aarch64
-          - target: armv7-unknown-linux-musleabihf
-            arch: armv7
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
@@ -308,7 +306,7 @@ jobs:
           manylinux: musllinux_1_2
           args: --release --out dist --features python-bindings
 
-      - uses: uraimo/run-on-arch-action@v2.5.0
+      - uses: uraimo/run-on-arch-action@v2.8.1
         name: Install built wheel
         with:
           arch: ${{ matrix.platform.arch }}

.github/workflows/rust.yml

@@ -207,23 +207,6 @@ jobs:
         #   AR=/opt/homebrew/opt/llvm/bin/llvm-ar CC=/opt/homebrew/opt/llvm/bin/clang wasm-pack test --firefox --headless -- -Z build-std="panic_abort,std" --features web
         run: wasm-pack test --chrome --headless -- -Z build-std="panic_abort,std" --features web
 
-  tutorial:
-    runs-on: ubuntu-latest
-    needs: [build, library-tests, docs, python-tests, python-integration-tests]
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
-        with:
-          toolchain: nightly-2024-07-18
-          override: true
-          components: rustfmt, clippy
-      - uses: baptiste0928/cargo-install@v1
-        with:
-          crate: cargo-nextest
-          locked: true
-      - name: Circuit Render
-        run: cargo nextest run --release --verbose tests::tutorial_
-
   mock-proving-tests:
     runs-on: non-gpu
     needs: [build, library-tests, docs, python-tests, python-integration-tests]
@@ -240,6 +223,8 @@ jobs:
           locked: true
       # - name: The Worm Mock
       #   run: cargo nextest run --release --verbose tests::large_mock_::large_tests_5_expects -- --include-ignored
+      - name: public outputs and bounded lookup log
+        run: cargo nextest run --release --verbose tests::mock_bounded_lookup_log --test-threads 32
       - name: public outputs and tolerance > 0
         run: cargo nextest run --release --verbose tests::mock_tolerance_public_outputs_ --test-threads 32
       - name: public outputs + batch size == 10
@@ -492,23 +477,23 @@ jobs:
       - name: Mock aggr tests (KZG)
         run: cargo nextest run --release --verbose tests_aggr::kzg_aggr_mock_prove_and_verify_  --test-threads 8
 
-  prove-and-verify-aggr-tests-gpu:
-    runs-on: GPU
-    env:
-      ENABLE_ICICLE_GPU: true
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
-        with:
-          toolchain: nightly-2024-07-18
-          override: true
-          components: rustfmt, clippy
-      - uses: baptiste0928/cargo-install@v1
-        with:
-          crate: cargo-nextest
-          locked: true
-      - name: KZG )tests
-        run: cargo nextest run --verbose tests_aggr::kzg_aggr_prove_and_verify_  --features icicle --test-threads 1 -- --include-ignored
+  # prove-and-verify-aggr-tests-gpu:
+  #   runs-on: GPU
+  #   env:
+  #     ENABLE_ICICLE_GPU: true
+  #   steps:
+  #     - uses: actions/checkout@v4
+  #     - uses: actions-rs/toolchain@v1
+  #       with:
+  #         toolchain: nightly-2024-07-18
+  #         override: true
+  #         components: rustfmt, clippy
+  #     - uses: baptiste0928/cargo-install@v1
+  #       with:
+  #         crate: cargo-nextest
+  #         locked: true
+  #     - name: KZG tests
+  #       run: cargo nextest run --verbose tests_aggr::kzg_aggr_prove_and_verify_  --features icicle --test-threads 1 -- --include-ignored
 
   prove-and-verify-aggr-tests:
     runs-on: large-self-hosted
@@ -612,8 +597,6 @@ jobs:
         run: python -m venv .env --clear; source .env/bin/activate; pip install -r requirements.txt;
       - name: Build python ezkl
         run: source .env/bin/activate; unset CONDA_PREFIX; maturin develop --features python-bindings --release
-      - name: Div rebase
-        run: source .env/bin/activate; cargo nextest run --release --verbose tests::accuracy_measurement_div_rebase_
       - name: Public inputs
         run: source .env/bin/activate; cargo nextest run --release --verbose tests::accuracy_measurement_public_inputs_
       - name: fixed params

.github/workflows/update-ios-package.yml

@@ -36,6 +36,15 @@ jobs:
           rm -rf ezkl-swift-package/Sources/EzklCoreBindings
           cp -r build/EzklCoreBindings ezkl-swift-package/Sources/
 
+      - name: Copy Test Files
+        run: |
+          rm -rf ezkl-swift-package/Tests/EzklAssets/*
+          
+          cp tests/assets/kzg ezkl-swift-package/Tests/EzklAssets/kzg.srs
+          cp tests/assets/input.json ezkl-swift-package/Tests/EzklAssets/input.json
+          cp tests/assets/model.compiled ezkl-swift-package/Tests/EzklAssets/network.ezkl
+          cp tests/assets/settings.json ezkl-swift-package/Tests/EzklAssets/settings.json
+
       - name: Set up Xcode environment
         run: |
           sudo xcode-select -s /Applications/Xcode.app/Contents/Developer
@@ -66,10 +75,11 @@ jobs:
           git config user.name "GitHub Action"
           git config user.email "action@github.com"
           git add Sources/EzklCoreBindings
+          git add Tests/EzklAssets
           git commit -m "Automatically updated EzklCoreBindings for EZKL"
           git tag ${{ github.event.inputs.tag }}
           git remote set-url origin https://zkonduit:${EZKL_PORTER_TOKEN}@github.com/zkonduit/ezkl-swift-package.git
           git push origin
-          git push origin --tags
+          git push origin tag ${{ github.event.inputs.tag }}
         env:
           EZKL_PORTER_TOKEN: ${{ secrets.EZKL_PORTER_TOKEN }}

Cargo.toml

@@ -20,7 +20,9 @@ halo2_gadgets = { git = "https://github.com/zkonduit/halo2", branch = "ac/option
 halo2curves = { git = "https://github.com/privacy-scaling-explorations/halo2curves", rev = "b753a832e92d5c86c5c997327a9cf9de86a18851", features = [
     "derive_serde",
 ] }
-halo2_proofs = { git = "https://github.com/zkonduit/halo2", package = "halo2_proofs", branch = "ac/cache-lookup-commitments", features = ["circuit-params"] }
+halo2_proofs = { git = "https://github.com/zkonduit/halo2", package = "halo2_proofs", branch = "ac/cache-lookup-commitments", features = [
+    "circuit-params",
+] }
 rand = { version = "0.8", default-features = false }
 itertools = { version = "0.10.3", default-features = false }
 clap = { version = "4.5.3", features = ["derive"], optional = true }
@@ -43,10 +45,7 @@ tosubcommand = { git = "https://github.com/zkonduit/enum_to_subcommand", package
 semver = { version = "1.0.22", optional = true }
 
 [target.'cfg(not(target_arch = "wasm32"))'.dependencies]
-serde_json = { version = "1.0.97", features = [
-    "float_roundtrip",
-    "raw_value",
-] }
+serde_json = { version = "1.0.97", features = ["float_roundtrip", "raw_value"] }
 
 # evm related deps
 alloy = { git = "https://github.com/alloy-rs/alloy", version = "0.1.0", rev = "5fbf57bac99edef9d8475190109a7ea9fb7e5e83", features = [
@@ -56,23 +55,39 @@ alloy = { git = "https://github.com/alloy-rs/alloy", version = "0.1.0", rev = "5
     "rpc-types-eth",
     "signer-wallet",
     "node-bindings",
-    
+
+], optional = true }
+foundry-compilers = { version = "0.4.1", features = [
+    "svm-solc",
 ], optional = true }
-foundry-compilers = { version = "0.4.1", features = ["svm-solc"], optional = true }
 ethabi = { version = "18", optional = true }
 indicatif = { version = "0.17.5", features = ["rayon"], optional = true }
 gag = { version = "1.0.0", default-features = false, optional = true }
 instant = { version = "0.1" }
-reqwest = { version = "0.12.4", default-features = false, features = ["default-tls", "multipart", "stream"], optional = true }
+reqwest = { version = "0.12.4", default-features = false, features = [
+    "default-tls",
+    "multipart",
+    "stream",
+], optional = true }
 openssl = { version = "0.10.55", features = ["vendored"], optional = true }
 tokio-postgres = { version = "0.7.10", optional = true }
 pg_bigdecimal = { version = "0.1.5", optional = true }
 lazy_static = { version = "1.4.0", optional = true }
 colored_json = { version = "3.0.1", default-features = false, optional = true }
 regex = { version = "1", default-features = false, optional = true }
-tokio = { version = "1.35.0", default-features = false, features = ["macros", "rt-multi-thread"], optional = true }
-pyo3 = { version = "0.21.2", features = ["extension-module", "abi3-py37", "macros"], default-features = false, optional = true }
-pyo3-asyncio = { git = "https://github.com/jopemachine/pyo3-asyncio/", branch="migration-pyo3-0.21", features = ["attributes", "tokio-runtime"], default-features = false, optional = true }
+tokio = { version = "1.35.0", default-features = false, features = [
+    "macros",
+    "rt-multi-thread",
+], optional = true }
+pyo3 = { version = "0.21.2", features = [
+    "extension-module",
+    "abi3-py37",
+    "macros",
+], default-features = false, optional = true }
+pyo3-asyncio = { git = "https://github.com/jopemachine/pyo3-asyncio/", branch = "migration-pyo3-0.21", features = [
+    "attributes",
+    "tokio-runtime",
+], default-features = false, optional = true }
 pyo3-log = { version = "0.10.0", default-features = false, optional = true }
 tract-onnx = { git = "https://github.com/sonos/tract/", rev = "40c64319291184814d9fea5fdf4fa16f5a4f7116", default-features = false, optional = true }
 tabled = { version = "0.12.0", optional = true }
@@ -197,7 +212,13 @@ required-features = ["ios-bindings", "uuid", "camino", "uniffi_bindgen"]
 
 [features]
 web = ["wasm-bindgen-rayon"]
-default = ["ezkl", "mv-lookup", "precompute-coset", "no-banner", "parallel-poly-read"]
+default = [
+    "ezkl",
+    "mv-lookup",
+    "precompute-coset",
+    "no-banner",
+    "parallel-poly-read",
+]
 onnx = ["dep:tract-onnx"]
 python-bindings = ["pyo3", "pyo3-log", "pyo3-asyncio"]
 ios-bindings = ["mv-lookup", "precompute-coset", "parallel-poly-read", "uniffi"]
@@ -231,7 +252,10 @@ ezkl = [
     "dep:clap",
     "dep:tosubcommand",
 ]
-parallel-poly-read = ["halo2_proofs/circuit-params", "halo2_proofs/parallel-poly-read"]
+parallel-poly-read = [
+    "halo2_proofs/circuit-params",
+    "halo2_proofs/parallel-poly-read",
+]
 mv-lookup = [
     "halo2_proofs/mv-lookup",
     "snark-verifier/mv-lookup",
@@ -260,4 +284,3 @@ rustflags = ["-C", "relocation-model=pic"]
 lto = "fat"
 codegen-units = 1
 # panic = "abort"
-

abis/DataAttestationSingle.json

@@ -0,0 +1,147 @@
+[
+    {
+        "inputs": [
+            {
+                "internalType": "address",
+                "name": "_contractAddresses",
+                "type": "address"
+            },
+            {
+                "internalType": "bytes",
+                "name": "_callData",
+                "type": "bytes"
+            },
+            {
+                "internalType": "uint256",
+                "name": "_decimals",
+                "type": "uint256"
+            },
+            {
+                "internalType": "uint256[]",
+                "name": "_scales",
+                "type": "uint256[]"
+            },
+            {
+                "internalType": "uint8",
+                "name": "_instanceOffset",
+                "type": "uint8"
+            },
+            {
+                "internalType": "address",
+                "name": "_admin",
+                "type": "address"
+            }
+        ],
+        "stateMutability": "nonpayable",
+        "type": "constructor"
+    },
+    {
+        "inputs": [],
+        "name": "accountCall",
+        "outputs": [
+            {
+                "internalType": "address",
+                "name": "contractAddress",
+                "type": "address"
+            },
+            {
+                "internalType": "bytes",
+                "name": "callData",
+                "type": "bytes"
+            },
+            {
+                "internalType": "uint256",
+                "name": "decimals",
+                "type": "uint256"
+            }
+        ],
+        "stateMutability": "view",
+        "type": "function"
+    },
+    {
+        "inputs": [],
+        "name": "admin",
+        "outputs": [
+            {
+                "internalType": "address",
+                "name": "",
+                "type": "address"
+            }
+        ],
+        "stateMutability": "view",
+        "type": "function"
+    },
+    {
+        "inputs": [],
+        "name": "instanceOffset",
+        "outputs": [
+            {
+                "internalType": "uint8",
+                "name": "",
+                "type": "uint8"
+            }
+        ],
+        "stateMutability": "view",
+        "type": "function"
+    },
+    {
+        "inputs": [
+            {
+                "internalType": "address",
+                "name": "_contractAddresses",
+                "type": "address"
+            },
+            {
+                "internalType": "bytes",
+                "name": "_callData",
+                "type": "bytes"
+            },
+            {
+                "internalType": "uint256",
+                "name": "_decimals",
+                "type": "uint256"
+            }
+        ],
+        "name": "updateAccountCalls",
+        "outputs": [],
+        "stateMutability": "nonpayable",
+        "type": "function"
+    },
+    {
+        "inputs": [
+            {
+                "internalType": "address",
+                "name": "_admin",
+                "type": "address"
+            }
+        ],
+        "name": "updateAdmin",
+        "outputs": [],
+        "stateMutability": "nonpayable",
+        "type": "function"
+    },
+    {
+        "inputs": [
+            {
+                "internalType": "address",
+                "name": "verifier",
+                "type": "address"
+            },
+            {
+                "internalType": "bytes",
+                "name": "encoded",
+                "type": "bytes"
+            }
+        ],
+        "name": "verifyWithDataAttestation",
+        "outputs": [
+            {
+                "internalType": "bool",
+                "name": "",
+                "type": "bool"
+            }
+        ],
+        "stateMutability": "view",
+        "type": "function"
+    }
+]

abis/QuantizeData.json

@@ -1,4 +1,23 @@
 [
+	{
+		"inputs": [
+			{
+				"internalType": "int256[]",
+				"name": "quantized_data",
+				"type": "int256[]"
+			}
+		],
+		"name": "check_is_valid_field_element",
+		"outputs": [
+			{
+				"internalType": "uint256[]",
+				"name": "output",
+				"type": "uint256[]"
+			}
+		],
+		"stateMutability": "pure",
+		"type": "function"
+	},
 	{
 		"inputs": [
 			{
@@ -17,12 +36,41 @@
 				"type": "uint256[]"
 			}
 		],
-		"name": "quantize_data",
+		"name": "quantize_data_multi",
 		"outputs": [
 			{
-				"internalType": "int64[]",
+				"internalType": "int256[]",
 				"name": "quantized_data",
-				"type": "int64[]"
+				"type": "int256[]"
+			}
+		],
+		"stateMutability": "pure",
+		"type": "function"
+	},
+	{
+		"inputs": [
+			{
+				"internalType": "bytes",
+				"name": "data",
+				"type": "bytes"
+			},
+			{
+				"internalType": "uint256",
+				"name": "decimals",
+				"type": "uint256"
+			},
+			{
+				"internalType": "uint256[]",
+				"name": "scales",
+				"type": "uint256[]"
+			}
+		],
+		"name": "quantize_data_single",
+		"outputs": [
+			{
+				"internalType": "int256[]",
+				"name": "quantized_data",
+				"type": "int256[]"
 			}
 		],
 		"stateMutability": "pure",

contracts/AttestData.sol

@@ -163,6 +163,253 @@ contract SwapProofCommitments {
     } /// end checkKzgCommits
 }
 
+contract DataAttestationSingle is LoadInstances, SwapProofCommitments {
+    /**
+     * @notice Struct used to make view only call to account to fetch the data that EZKL reads from.
+     * @param the address of the account to make calls to
+     * @param the abi encoded function calls to make to the `contractAddress`
+     */
+    struct AccountCall {
+        address contractAddress;
+        bytes callData;
+        uint256 decimals;
+    }
+    AccountCall public accountCall;
+
+    uint[] scales;
+
+    address public admin;
+
+    /**
+     * @notice EZKL P value
+     * @dev In order to prevent the verifier from accepting two version of the same pubInput, n and the quantity (n + P),  where n + P <= 2^256, we require that all instances are stricly less than P. a
+     * @dev The reason for this is that the assmebly code of the verifier performs all arithmetic operations modulo P and as a consequence can't distinguish between n and n + P.
+     */
+    uint256 constant ORDER =
+        uint256(
+            0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001
+        );
+
+    uint256 constant INPUT_LEN = 0;
+
+    uint256 constant OUTPUT_LEN = 0;
+
+    uint8 public instanceOffset;
+
+    /**
+     * @dev Initialize the contract with account calls the EZKL model will read from.
+     * @param _contractAddresses - The calls to all the contracts EZKL reads storage from.
+     * @param _callData - The abi encoded function calls to make to the `contractAddress` that EZKL reads storage from.
+     */
+    constructor(
+        address _contractAddresses,
+        bytes memory _callData,
+        uint256 _decimals,
+        uint[] memory _scales,
+        uint8 _instanceOffset,
+        address _admin
+    ) {
+        admin = _admin;
+        for (uint i; i < _scales.length; i++) {
+            scales.push(1 << _scales[i]);
+        }
+        populateAccountCalls(_contractAddresses, _callData, _decimals);
+        instanceOffset = _instanceOffset;
+    }
+
+    function updateAdmin(address _admin) external {
+        require(msg.sender == admin, "Only admin can update admin");
+        if (_admin == address(0)) {
+            revert();
+        }
+        admin = _admin;
+    }
+
+    function updateAccountCalls(
+        address _contractAddresses,
+        bytes memory _callData,
+        uint256 _decimals
+    ) external {
+        require(msg.sender == admin, "Only admin can update account calls");
+        populateAccountCalls(_contractAddresses, _callData, _decimals);
+    }
+
+    function populateAccountCalls(
+        address _contractAddresses,
+        bytes memory _callData,
+        uint256 _decimals
+    ) internal {
+        AccountCall memory _accountCall = accountCall;
+        _accountCall.contractAddress = _contractAddresses;
+        _accountCall.callData = _callData;
+        _accountCall.decimals = 10 ** _decimals;
+        accountCall = _accountCall;
+    }
+
+    function mulDiv(
+        uint256 x,
+        uint256 y,
+        uint256 denominator
+    ) internal pure returns (uint256 result) {
+        unchecked {
+            uint256 prod0;
+            uint256 prod1;
+            assembly {
+                let mm := mulmod(x, y, not(0))
+                prod0 := mul(x, y)
+                prod1 := sub(sub(mm, prod0), lt(mm, prod0))
+            }
+
+            if (prod1 == 0) {
+                return prod0 / denominator;
+            }
+
+            require(denominator > prod1, "Math: mulDiv overflow");
+
+            uint256 remainder;
+            assembly {
+                remainder := mulmod(x, y, denominator)
+                prod1 := sub(prod1, gt(remainder, prod0))
+                prod0 := sub(prod0, remainder)
+            }
+
+            uint256 twos = denominator & (~denominator + 1);
+            assembly {
+                denominator := div(denominator, twos)
+                prod0 := div(prod0, twos)
+                twos := add(div(sub(0, twos), twos), 1)
+            }
+
+            prod0 |= prod1 * twos;
+
+            uint256 inverse = (3 * denominator) ^ 2;
+
+            inverse *= 2 - denominator * inverse;
+            inverse *= 2 - denominator * inverse;
+            inverse *= 2 - denominator * inverse;
+            inverse *= 2 - denominator * inverse;
+            inverse *= 2 - denominator * inverse;
+            inverse *= 2 - denominator * inverse;
+
+            result = prod0 * inverse;
+            return result;
+        }
+    }
+    /**
+     * @dev Quantize the data returned from the account calls to the scale used by the EZKL model.
+     * @param x - One of the elements of the data returned from the account calls
+     * @param _decimals - Number of base 10 decimals to scale the data by.
+     * @param _scale - The base 2 scale used to convert the floating point value into a fixed point value.
+     *
+     */
+    function quantizeData(
+        int x,
+        uint256 _decimals,
+        uint256 _scale
+    ) internal pure returns (int256 quantized_data) {
+        bool neg = x < 0;
+        if (neg) x = -x;
+        uint output = mulDiv(uint256(x), _scale, _decimals);
+        if (mulmod(uint256(x), _scale, _decimals) * 2 >= _decimals) {
+            output += 1;
+        }
+        quantized_data = neg ? -int256(output) : int256(output);
+    }
+    /**
+     * @dev Make a static call to the account to fetch the data that EZKL reads from.
+     * @param target - The address of the account to make calls to.
+     * @param data  - The abi encoded function calls to make to the `contractAddress` that EZKL reads storage from.
+     * @return The data returned from the account calls. (Must come from either a view or pure function. Will throw an error otherwise)
+     */
+    function staticCall(
+        address target,
+        bytes memory data
+    ) internal view returns (bytes memory) {
+        (bool success, bytes memory returndata) = target.staticcall(data);
+        if (success) {
+            if (returndata.length == 0) {
+                require(
+                    target.code.length > 0,
+                    "Address: call to non-contract"
+                );
+            }
+            return returndata;
+        } else {
+            revert("Address: low-level call failed");
+        }
+    }
+    /**
+     * @dev Convert the fixed point quantized data into a field element.
+     * @param x - The quantized data.
+     * @return field_element - The field element.
+     */
+    function toFieldElement(
+        int256 x
+    ) internal pure returns (uint256 field_element) {
+        // The casting down to uint256 is safe because the order is about 2^254, and the value
+        // of x ranges of -2^127 to 2^127, so x + int(ORDER) is always positive.
+        return uint256(x + int(ORDER)) % ORDER;
+    }
+
+    /**
+     * @dev Make the account calls to fetch the data that EZKL reads from and attest to the data.
+     * @param instances - The public instances to the proof (the data in the proof that publicly accessible to the verifier).
+     */
+    function attestData(uint256[] memory instances) internal view {
+        require(
+            instances.length >= INPUT_LEN + OUTPUT_LEN,
+            "Invalid public inputs length"
+        );
+        AccountCall memory _accountCall = accountCall;
+        uint[] memory _scales = scales;
+        bytes memory returnData = staticCall(
+            _accountCall.contractAddress,
+            _accountCall.callData
+        );
+        int256[] memory x = abi.decode(returnData, (int256[]));
+        uint _offset;
+        int output = quantizeData(x[0], _accountCall.decimals, _scales[0]);
+        uint field_element = toFieldElement(output);
+        for (uint i = 0; i < x.length; i++) {
+            if (field_element != instances[i + instanceOffset]) {
+                _offset += 1;
+            } else {
+                break;
+            }
+        }
+        uint length = x.length - _offset;
+        for (uint i = 1; i < length; i++) {
+            output = quantizeData(x[i], _accountCall.decimals, _scales[i]);
+            field_element = toFieldElement(output);
+            require(
+                field_element == instances[i + instanceOffset + _offset],
+                "Public input does not match"
+            );
+        }
+    }
+
+    /**
+     * @dev Verify the proof with the data attestation.
+     * @param verifier - The address of the verifier contract.
+     * @param encoded - The verifier calldata.
+     */
+    function verifyWithDataAttestation(
+        address verifier,
+        bytes calldata encoded
+    ) public view returns (bool) {
+        require(verifier.code.length > 0, "Address: call to non-contract");
+        attestData(getInstancesCalldata(encoded));
+        // static call the verifier contract to verify the proof
+        (bool success, bytes memory returndata) = verifier.staticcall(encoded);
+
+        if (success) {
+            return abi.decode(returndata, (bool));
+        } else {
+            revert("low-level call to verifier failed");
+        }
+    }
+}
+
 // This contract serves as a Data Attestation Verifier for the EZKL model.
 // It is designed to read and attest to instances of proofs generated from a specified circuit.
 // It is particularly constructed to read only int256 data from specified on-chain contracts' view functions.
@@ -173,11 +420,11 @@ contract SwapProofCommitments {
 // 3. Static Calls: Makes static calls to fetch data from other contracts. See the `staticCall` method.
 // 4. Field Element Conversion: The fixed-point representation is then converted into a field element modulo P using the `toFieldElement` method.
 // 5. Data Attestation: The `attestData` method validates that the public instances match the data fetched and processed by the contract.
-// 6. Proof Verification: The `verifyWithDataAttestation` method parses the instances out of the encoded calldata and calls the `attestData` method to validate the public instances,
+// 6. Proof Verification: The `verifyWithDataAttestationMulti` method parses the instances out of the encoded calldata and calls the `attestData` method to validate the public instances,
 // 6b. Optional KZG Commitment Verification: It also checks the KZG commitments in the proof against the expected commitments using the `checkKzgCommits` method.
 //  then calls the `verifyProof` method to verify the proof on the verifier.
 
-contract DataAttestation is LoadInstances, SwapProofCommitments {
+contract DataAttestationMulti is LoadInstances, SwapProofCommitments {
     /**
      * @notice Struct used to make view only calls to accounts to fetch the data that EZKL reads from.
      * @param the address of the account to make calls to

docs/python/requirements-docs.txt

@@ -1,4 +1,4 @@
-ezkl==15.2.0
+ezkl
 sphinx
 sphinx-rtd-theme
 sphinxcontrib-napoleon

docs/python/src/conf.py

@@ -1,7 +1,7 @@
 import ezkl
 
 project = 'ezkl'
-release = '15.2.0'
+release = '0.0.0'
 version = release
 
 

examples/notebooks/data_attest.ipynb

@@ -592,7 +592,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.12.2"
+            "version": "3.12.5"
         },
         "orig_nbformat": 4
     },

examples/notebooks/data_attest_hashed.ipynb

@@ -648,10 +648,10 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.7"
         },
         "orig_nbformat": 4
     },
     "nbformat": 4,
     "nbformat_minor": 2
-}
+}

examples/notebooks/logistic_regression.ipynb

@@ -271,7 +271,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.12.2"
+            "version": "3.12.7"
         }
     },
     "nbformat": 4,

examples/notebooks/simple_demo_aggregated_proofs.ipynb

@@ -171,7 +171,7 @@
                 "json.dump(data, open(cal_path, 'w'))\n",
                 "\n",
                 "\n",
-                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
             ]
         },
         {
@@ -328,7 +328,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 26,
             "id": "171702d3",
             "metadata": {},
             "outputs": [],
@@ -348,7 +348,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 27,
             "id": "671dfdd5",
             "metadata": {},
             "outputs": [],
@@ -364,7 +364,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 28,
             "id": "50eba2f4",
             "metadata": {},
             "outputs": [],
@@ -399,9 +399,9 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.7"
         }
     },
     "nbformat": 4,
     "nbformat_minor": 5
-}
+}

examples/notebooks/univ3-da.ipynb

@@ -0,0 +1,763 @@
+{
+    "cells": [
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# univ3-da-ezkl\n",
+                "\n",
+                "Here's an example leveraging EZKL whereby the inputs to the model are read and attested to from an on-chain source. For this setup we make a single call to a view function that returns an array of UniV3 historical TWAP price data that we will attest to on-chain. \n"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "First we import the necessary dependencies and set up logging to be as informative as possible. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# check if notebook is in colab\n",
+                "try:\n",
+                "    # install ezkl\n",
+                "    import google.colab\n",
+                "    import subprocess\n",
+                "    import sys\n",
+                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
+                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
+                "\n",
+                "# rely on local installation of ezkl if the notebook is not in colab\n",
+                "except:\n",
+                "    pass\n",
+                "\n",
+                "\n",
+                "from torch import nn\n",
+                "import ezkl\n",
+                "import os\n",
+                "import json\n",
+                "import logging\n",
+                "\n",
+                "# uncomment for more descriptive logging \n",
+                "FORMAT = '%(levelname)s %(name)s %(asctime)-15s %(filename)s:%(lineno)d %(message)s'\n",
+                "logging.basicConfig(format=FORMAT)\n",
+                "logging.getLogger().setLevel(logging.DEBUG)\n"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Now we define our model. It is a very simple PyTorch model that has just one layer, an average pooling 2D layer. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import torch\n",
+                "# Defines the model\n",
+                "\n",
+                "class MyModel(nn.Module):\n",
+                "    def __init__(self):\n",
+                "        super(MyModel, self).__init__()\n",
+                "        self.layer = nn.AvgPool2d(2, 1, (1, 1))\n",
+                "\n",
+                "    def forward(self, x):\n",
+                "        return self.layer(x)[0]\n",
+                "\n",
+                "\n",
+                "circuit = MyModel()\n",
+                "\n",
+                "# this is where you'd train your model"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "We omit training for purposes of this demonstration. We've marked where training would happen in the cell above. \n",
+                "Now we export the model to onnx and create a corresponding (randomly generated) input. This input data will eventually be stored on chain and read from according to the call_data field in the graph input.\n",
+                "\n",
+                "You can replace the random `x` with real data if you so wish. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "x = 0.1*torch.rand(1,*[3, 2, 2], requires_grad=True)\n",
+                "\n",
+                "# Flips the neural net into inference mode\n",
+                "circuit.eval()\n",
+                "\n",
+                "    # Export the model\n",
+                "torch.onnx.export(circuit,               # model being run\n",
+                "                      x,                   # model input (or a tuple for multiple inputs)\n",
+                "                      \"network.onnx\",            # where to save the model (can be a file or file-like object)\n",
+                "                      export_params=True,        # store the trained parameter weights inside the model file\n",
+                "                      opset_version=10,          # the ONNX version to export the model to\n",
+                "                      do_constant_folding=True,  # whether to execute constant folding for optimization\n",
+                "                      input_names = ['input'],   # the model's input names\n",
+                "                      output_names = ['output'], # the model's output names\n",
+                "                      dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes\n",
+                "                                    'output' : {0 : 'batch_size'}})\n",
+                "\n",
+                "data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n",
+                "\n",
+                "data = dict(input_data = [data_array])\n",
+                "\n",
+                "    # Serialize data into file:\n",
+                "json.dump(data, open(\"input.json\", 'w' ))\n",
+                "\n"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "We now define a function that will create a new anvil instance which we will deploy our test contract too. This contract will contain in its storage the data that we will read from and attest to. In production you would not need to set up a local anvil instance. Instead you would replace RPC_URL with the actual RPC endpoint of the chain you are deploying your verifiers too, reading from the data on said chain."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 44,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import subprocess\n",
+                "import time\n",
+                "import threading\n",
+                "\n",
+                "# make sure anvil is running locally\n",
+                "# $ anvil -p 3030\n",
+                "\n",
+                "RPC_URL = \"http://localhost:3030\"\n",
+                "\n",
+                "# Save process globally\n",
+                "anvil_process = None\n",
+                "\n",
+                "def start_anvil():\n",
+                "    global anvil_process\n",
+                "    if anvil_process is None:\n",
+                "        anvil_process = subprocess.Popen([\"anvil\", \"-p\", \"3030\", \"--fork-url\", \"https://arb1.arbitrum.io/rpc\", \"--code-size-limit=41943040\"])\n",
+                "        if anvil_process.returncode is not None:\n",
+                "            raise Exception(\"failed to start anvil process\")\n",
+                "        time.sleep(3)\n",
+                "\n",
+                "def stop_anvil():\n",
+                "    global anvil_process\n",
+                "    if anvil_process is not None:\n",
+                "        anvil_process.terminate()\n",
+                "        anvil_process = None\n"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "We define our `PyRunArgs` objects which contains the visibility parameters for out model. \n",
+                "- `input_visibility` defines the visibility of the model inputs\n",
+                "- `param_visibility` defines the visibility of the model weights and constants and parameters \n",
+                "- `output_visibility` defines the visibility of the model outputs\n",
+                "\n",
+                "Here we create the following setup:\n",
+                "- `input_visibility`: \"public\"\n",
+                "- `param_visibility`: \"private\"\n",
+                "- `output_visibility`: public\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import ezkl\n",
+                "\n",
+                "model_path = os.path.join('network.onnx')\n",
+                "compiled_model_path = os.path.join('network.compiled')\n",
+                "pk_path = os.path.join('test.pk')\n",
+                "vk_path = os.path.join('test.vk')\n",
+                "settings_path = os.path.join('settings.json')\n",
+                "srs_path = os.path.join('kzg.srs')\n",
+                "data_path = os.path.join('input.json')\n",
+                "\n",
+                "run_args = ezkl.PyRunArgs()\n",
+                "run_args.input_visibility = \"public\"\n",
+                "run_args.param_visibility = \"private\"\n",
+                "run_args.output_visibility = \"public\"\n",
+                "run_args.num_inner_cols = 1\n",
+                "run_args.variables = [(\"batch_size\", 1)]"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Now we generate a settings file. This file basically instantiates a bunch of parameters that determine their circuit shape, size etc... Because of the way we represent nonlinearities in the circuit (using Halo2's [lookup tables](https://zcash.github.io/halo2/design/proving-system/lookup.html)), it is often best to _calibrate_ this settings file as some data can fall out of range of these lookups.\n",
+                "\n",
+                "You can pass a dataset for calibration that will be representative of real inputs you might find if and when you deploy the prover. Here we create a dummy calibration dataset for demonstration purposes. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# TODO: Dictionary outputs\n",
+                "res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
+                "assert res == True"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# generate a bunch of dummy calibration data\n",
+                "cal_data = {\n",
+                "    \"input_data\": [(0.1*torch.rand(2, *[3, 2, 2])).flatten().tolist()],\n",
+                "}\n",
+                "\n",
+                "cal_path = os.path.join('val_data.json')\n",
+                "# save as json file\n",
+                "with open(cal_path, \"w\") as f:\n",
+                "    json.dump(cal_data, f)\n",
+                "\n",
+                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
+                "assert res == True"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "The graph input for on chain data sources is formatted completely differently compared to file based data sources.\n",
+                "\n",
+                "- For file data sources, the raw floating point values that eventually get quantized, converted into field elements and stored in `witness.json` to be consumed by the circuit are stored. The output data contains the expected floating point values returned as outputs from running your vanilla pytorch model on the given inputs.\n",
+                "- For on chain data sources, the input_data field contains all the data necessary to read and format the on chain data into something digestable by EZKL (aka field elements :-D). \n",
+                "Here is what the schema for an on-chain data source graph input file should look like for a single call data source:\n",
+                "    \n",
+                "```json\n",
+                "{\n",
+                "    \"input_data\": {\n",
+                "        \"rpc\": \"http://localhost:3030\", // The rpc endpoint of the chain you are deploying your verifier to\n",
+                "        \"calls\": {\n",
+                "            \"call_data\": \"1f3be514000000000000000000000000c6962004f452be9203591991d15f6b388e09e8d00000000000000000000000000000000000000000000000000000000000000040000000000000000000000000000000000000000000000000000000000000000c000000000000000000000000000000000000000000000000000000000000000b000000000000000000000000000000000000000000000000000000000000000a0000000000000000000000000000000000000000000000000000000000000009000000000000000000000000000000000000000000000000000000000000000800000000000000000000000000000000000000000000000000000000000000070000000000000000000000000000000000000000000000000000000000000006000000000000000000000000000000000000000000000000000000000000000500000000000000000000000000000000000000000000000000000000000000040000000000000000000000000000000000000000000000000000000000000003000000000000000000000000000000000000000000000000000000000000000200000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000000000000000000000000000000000\", // The abi encoded call data to a view function that returns an array of on-chain data points we are attesting to. \n",
+                "            \"decimals\": 0, // The number of decimal places of the large uint256 value. This is our way of representing large wei values as floating points on chain, since the evm only natively supports integer values.\n",
+                "            \"address\": \"9A213F53334279C128C37DA962E5472eCD90554f\", // The address of the contract that we are calling to get the data. \n",
+                "            \"len\": 12 // The number of data points returned by the view function (the length of the array)\n",
+                "        }\n",
+                "    }\n",
+                "}\n",
+                "```"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "from web3 import Web3, HTTPProvider\n",
+                "from solcx import compile_standard\n",
+                "from decimal import Decimal\n",
+                "import json\n",
+                "import os\n",
+                "import torch\n",
+                "import requests\n",
+                "\n",
+                "# This function counts the decimal places of a floating point number\n",
+                "def count_decimal_places(num):\n",
+                "    num_str = str(num)\n",
+                "    if '.' in num_str:\n",
+                "        return len(num_str) - 1 - num_str.index('.')\n",
+                "    else:\n",
+                "        return 0\n",
+                "\n",
+                "# setup web3 instance\n",
+                "w3 = Web3(HTTPProvider(RPC_URL)) \n",
+                "\n",
+                "def set_next_block_timestamp(anvil_url, timestamp):\n",
+                "    # Send the JSON-RPC request to Anvil\n",
+                "    payload = {\n",
+                "        \"jsonrpc\": \"2.0\",\n",
+                "        \"id\": 1,\n",
+                "        \"method\": \"evm_setNextBlockTimestamp\",\n",
+                "        \"params\": [timestamp]\n",
+                "    }\n",
+                "    response = requests.post(anvil_url, json=payload)\n",
+                "    if response.status_code == 200:\n",
+                "        print(f\"Next block timestamp set to: {timestamp}\")\n",
+                "    else:\n",
+                "        print(f\"Failed to set next block timestamp: {response.text}\")\n",
+                "\n",
+                "def on_chain_data(tensor):\n",
+                "    # Step 0: Convert the tensor to a flat list\n",
+                "    data = tensor.view(-1).tolist()\n",
+                "\n",
+                "    # Step 1: Prepare the calldata\n",
+                "    secondsAgo = [len(data) - 1 - i for i in range(len(data))]\n",
+                "\n",
+                "    # Step 2: Prepare and compile the contract UniTickAttestor contract\n",
+                "    contract_source_code = '''\n",
+                "    // SPDX-License-Identifier: MIT\n",
+                "    pragma solidity ^0.8.20;\n",
+                "\n",
+                "    /// @title Pool state that is not stored\n",
+                "    /// @notice Contains view functions to provide information about the pool that is computed rather than stored on the\n",
+                "    /// blockchain. The functions here may have variable gas costs.\n",
+                "    interface IUniswapV3PoolDerivedState {\n",
+                "        /// @notice Returns the cumulative tick and liquidity as of each timestamp `secondsAgo` from the current block timestamp\n",
+                "        /// @dev To get a time weighted average tick or liquidity-in-range, you must call this with two values, one representing\n",
+                "        /// the beginning of the period and another for the end of the period. E.g., to get the last hour time-weighted average tick,\n",
+                "        /// you must call it with secondsAgos = [3600, 0].\n",
+                "        /// log base sqrt(1.0001) of token1 / token0. The TickMath library can be used to go from a tick value to a ratio.\n",
+                "        /// @dev The time weighted average tick represents the geometric time weighted average price of the pool, in\n",
+                "        /// @param secondsAgos From how long ago each cumulative tick and liquidity value should be returned\n",
+                "        /// @return tickCumulatives Cumulative tick values as of each `secondsAgos` from the current block timestamp\n",
+                "        /// @return secondsPerLiquidityCumulativeX128s Cumulative seconds per liquidity-in-range value as of each `secondsAgos` from the current block\n",
+                "        /// timestamp\n",
+                "        function observe(\n",
+                "            uint32[] calldata secondsAgos\n",
+                "        )\n",
+                "            external\n",
+                "            view\n",
+                "            returns (\n",
+                "                int56[] memory tickCumulatives,\n",
+                "                uint160[] memory secondsPerLiquidityCumulativeX128s\n",
+                "            );\n",
+                "    }\n",
+                "\n",
+                "    /// @title Uniswap Wrapper around `pool.observe` that stores the parameters for fetching and then attesting to historical data\n",
+                "    /// @notice Provides functions to integrate with V3 pool oracle\n",
+                "    contract UniTickAttestor {\n",
+                "        /**\n",
+                "        * @notice Calculates time-weighted means of tick and liquidity for a given Uniswap V3 pool\n",
+                "        * @param pool Address of the pool that we want to observe\n",
+                "        * @param secondsAgo Number of seconds in the past from which to calculate the time-weighted means\n",
+                "        * @return tickCumulatives The cumulative tick values as of each `secondsAgo` from the current block timestamp\n",
+                "        */\n",
+                "        function consult(\n",
+                "            IUniswapV3PoolDerivedState pool,\n",
+                "            uint32[] memory secondsAgo\n",
+                "        ) public view returns (int256[] memory tickCumulatives) {\n",
+                "            tickCumulatives = new int256[](secondsAgo.length);\n",
+                "            (int56[] memory _ticks,) = pool.observe(secondsAgo);\n",
+                "            for (uint256 i = 0; i < secondsAgo.length; i++) {\n",
+                "                tickCumulatives[i] = int256(_ticks[i]);\n",
+                "            }\n",
+                "        }\n",
+                "    }\n",
+                "    '''\n",
+                "\n",
+                "    compiled_sol = compile_standard({\n",
+                "        \"language\": \"Solidity\",\n",
+                "        \"sources\": {\"UniTickAttestor.sol\": {\"content\": contract_source_code}},\n",
+                "        \"settings\": {\"outputSelection\": {\"*\": {\"*\": [\"metadata\", \"evm.bytecode\", \"abi\"]}}}\n",
+                "    })\n",
+                "\n",
+                "    # Get bytecode\n",
+                "    bytecode = compiled_sol['contracts']['UniTickAttestor.sol']['UniTickAttestor']['evm']['bytecode']['object']\n",
+                "\n",
+                "    # Get ABI\n",
+                "    # In production if you are reading from really large contracts you can just use\n",
+                "    # a stripped down version of the ABI of the contract you are calling, containing only the view functions you will fetch data from.\n",
+                "    abi = json.loads(compiled_sol['contracts']['UniTickAttestor.sol']['UniTickAttestor']['metadata'])['output']['abi']\n",
+                "\n",
+                "    # Step 3: Deploy the contract\n",
+                "    UniTickAttestor = w3.eth.contract(abi=abi, bytecode=bytecode)\n",
+                "    tx_hash = UniTickAttestor.constructor().transact()\n",
+                "    tx_receipt = w3.eth.wait_for_transaction_receipt(tx_hash)\n",
+                "    # If you are deploying to production you can skip the 3 lines of code above and just instantiate the contract like this,\n",
+                "    # passing the address and abi of the contract you are fetching data from.\n",
+                "    contract = w3.eth.contract(address=tx_receipt['contractAddress'], abi=abi)\n",
+                "\n",
+                "    # Step 4: Interact with the contract\n",
+                "    call = contract.functions.consult(\n",
+                "        # Address of the UniV3 usdc-weth pool 0.005 fee\n",
+                "        \"0xC6962004f452bE9203591991D15f6b388e09E8D0\",\n",
+                "        secondsAgo\n",
+                "    ).build_transaction()\n",
+                "    result = contract.functions.consult(\n",
+                "        # Address of the UniV3 usdc-weth pool 0.005 fee\n",
+                "        \"0xC6962004f452bE9203591991D15f6b388e09E8D0\",\n",
+                "        secondsAgo\n",
+                "    ).call()\n",
+                "    \n",
+                "    print(f'result: {result}')\n",
+                "    calldata = call['data'][2:]\n",
+                "\n",
+                "    time_stamp = w3.eth.get_block('latest')['timestamp']\n",
+                "\n",
+                "    print(f'time_stamp: {time_stamp}')\n",
+                "\n",
+                "    # Set the next block timestamp using the fetched time_stamp\n",
+                "    set_next_block_timestamp(RPC_URL, time_stamp)\n",
+                "\n",
+                "\n",
+                "    # Prepare the calls_to_account object\n",
+                "    # If you were calling view functions across multiple contracts,\n",
+                "    # you would have multiple entries in the calls_to_account array,\n",
+                "    # one for each contract.\n",
+                "    call_to_account = {\n",
+                "        'call_data': calldata,\n",
+                "        'decimals': 0,\n",
+                "        'address': contract.address[2:], # remove the '0x' prefix\n",
+                "        'len': len(data),\n",
+                "    }\n",
+                "\n",
+                "    print(f'call_to_account: {call_to_account}')\n",
+                "\n",
+                "    return call_to_account\n",
+                "\n",
+                "# Now let's start the Anvil process. You don't need to do this if you are deploying to a non-local chain.\n",
+                "start_anvil()\n",
+                "\n",
+                "# Now let's call our function, passing in the same input tensor we used to export the model 2 cells above.\n",
+                "calls_to_account = on_chain_data(x)\n",
+                "\n",
+                "data = dict(input_data =  {'rpc': RPC_URL, 'calls': calls_to_account })\n",
+                "\n",
+                "# Serialize on-chain data into file:\n",
+                "json.dump(data, open(\"input.json\", 'w'))"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "As we use Halo2 with KZG-commitments we need an SRS string from (preferably) a multi-party trusted setup ceremony. For an overview of the procedures for such a ceremony check out [this page](https://blog.ethereum.org/2023/01/16/announcing-kzg-ceremony). The `get_srs` command retrieves a correctly sized SRS given the calibrated settings file from [here](https://github.com/han0110/halo2-kzg-srs). \n",
+                "\n",
+                "These SRS were generated with [this](https://github.com/privacy-scaling-explorations/perpetualpowersoftau) ceremony. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "res = await ezkl.get_srs( settings_path)"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "We now need to generate the circuit witness. These are the model outputs (and any hashes) that are generated when feeding the previously generated `input.json` through the circuit / model. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# !export RUST_BACKTRACE=1\n",
+                "\n",
+                "witness_path = \"witness.json\"\n",
+                "\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Here we setup verifying and proving keys for the circuit. As the name suggests the proving key is needed for ... proving and the verifying key is needed for ... verifying. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# HERE WE SETUP THE CIRCUIT PARAMS\n",
+                "# WE GOT KEYS\n",
+                "# WE GOT CIRCUIT PARAMETERS\n",
+                "# EVERYTHING ANYONE HAS EVER NEEDED FOR ZK\n",
+                "res = ezkl.setup(\n",
+                "        compiled_model_path,\n",
+                "        vk_path,\n",
+                "        pk_path,\n",
+                "    )\n",
+                "\n",
+                "assert res == True\n",
+                "assert os.path.isfile(vk_path)\n",
+                "assert os.path.isfile(pk_path)\n",
+                "assert os.path.isfile(settings_path)"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Now we generate a full proof. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# GENERATE A PROOF\n",
+                "\n",
+                "proof_path = os.path.join('test.pf')\n",
+                "\n",
+                "res = ezkl.prove(\n",
+                "        witness_path,\n",
+                "        compiled_model_path,\n",
+                "        pk_path,\n",
+                "        proof_path,\n",
+                "        \"single\",\n",
+                "    )\n",
+                "\n",
+                "print(res)\n",
+                "assert os.path.isfile(proof_path)"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "And verify it as a sanity check. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# VERIFY IT\n",
+                "\n",
+                "res = ezkl.verify(\n",
+                "        proof_path,\n",
+                "        settings_path,\n",
+                "        vk_path,\n",
+                "    )\n",
+                "\n",
+                "assert res == True\n",
+                "print(\"verified\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "We can now create and then deploy a vanilla evm verifier."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "abi_path = 'test.abi'\n",
+                "sol_code_path = 'test.sol'\n",
+                "\n",
+                "res = await ezkl.create_evm_verifier(\n",
+                "        vk_path,\n",
+                "        settings_path,\n",
+                "        sol_code_path,\n",
+                "        abi_path,\n",
+                "    )\n",
+                "assert res == True"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import json\n",
+                "\n",
+                "addr_path_verifier = \"addr_verifier.txt\"\n",
+                "\n",
+                "res = await ezkl.deploy_evm(\n",
+                "    addr_path_verifier,\n",
+                "    sol_code_path,\n",
+                "    'http://127.0.0.1:3030'\n",
+                ")\n",
+                "\n",
+                "assert res == True"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "With the vanilla verifier deployed, we can now create the data attestation contract, which will read in the instances from the calldata to the verifier, attest to them, call the verifier and then return the result. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "\n",
+                "abi_path = 'test.abi'\n",
+                "sol_code_path = 'test.sol'\n",
+                "input_path = 'input.json'\n",
+                "\n",
+                "res = await ezkl.create_evm_data_attestation(\n",
+                "        input_path,\n",
+                "        settings_path,\n",
+                "        sol_code_path,\n",
+                "        abi_path,\n",
+                "    )"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Now we can deploy the data attest verifier contract. For security reasons, this binding will only deploy to a local anvil instance, using accounts generated by anvil. \n",
+                "So should only be used for testing purposes."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "addr_path_da = \"addr_da.txt\"\n",
+                "\n",
+                "res = await ezkl.deploy_da_evm(\n",
+                "        addr_path_da,\n",
+                "        input_path,\n",
+                "        settings_path,\n",
+                "        sol_code_path,\n",
+                "        RPC_URL,\n",
+                "    )\n"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Here we need to regenerate the witness, prove and then verify all within the same cell. This is because we want to reduce the amount of latency between reading on-chain state and verifying it on-chain. This is because the attest input values read from the oracle are time sensitive (their values are derived from computing on block.timestamp) and can change between the time of reading and the time of verifying.\n",
+                "\n",
+                "Call the view only verify method on the contract to verify the proof. Since it is a view function this is safe to use in production since you don't have to pass your private key."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# !export RUST_BACKTRACE=1\n",
+                "\n",
+                "calls_to_account = on_chain_data(x)\n",
+                "\n",
+                "data = dict(input_data =  {'rpc': RPC_URL, 'calls': calls_to_account })\n",
+                "\n",
+                "# Serialize on-chain data into file:\n",
+                "json.dump(data, open(\"input.json\", 'w'))\n",
+                "\n",
+                "# setup web3 instance\n",
+                "w3 = Web3(HTTPProvider(RPC_URL)) \n",
+                "\n",
+                "time_stamp = w3.eth.get_block('latest')['timestamp']\n",
+                "\n",
+                "print(f'time_stamp: {time_stamp}')\n",
+                "\n",
+                "\n",
+                "witness_path = \"witness.json\"\n",
+                "\n",
+                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "\n",
+                "res = ezkl.prove(\n",
+                "        witness_path,\n",
+                "        compiled_model_path,\n",
+                "        pk_path,\n",
+                "        proof_path,\n",
+                "        \"single\",\n",
+                "    )\n",
+                "\n",
+                "print(res)\n",
+                "assert os.path.isfile(proof_path)\n",
+                "# read the verifier address\n",
+                "addr_verifier = None\n",
+                "with open(addr_path_verifier, 'r') as f:\n",
+                "    addr = f.read()\n",
+                "#read the data attestation address\n",
+                "addr_da = None\n",
+                "with open(addr_path_da, 'r') as f:\n",
+                "    addr_da = f.read()\n",
+                "\n",
+                "res = await ezkl.verify_evm(\n",
+                "    addr,\n",
+                "    proof_path,\n",
+                "    RPC_URL,\n",
+                "    addr_da,\n",
+                ")"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": ".env",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.11.5"
+        },
+        "orig_nbformat": 4
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
+}

examples/onnx/exp/gen.py

@@ -0,0 +1,42 @@
+from torch import nn
+import torch
+import json
+import numpy as np
+
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+
+    def forward(self, x):          
+        m = torch.exp(x)
+        
+        return m 
+
+
+circuit = MyModel()
+
+x = torch.empty(1, 8).uniform_(0, 1)
+
+out = circuit(x)
+
+print(out)
+
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=17,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  input_names=['input'],   # the model's input names
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
+                                'output': {0: 'batch_size'}})
+
+
+d1 = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data = dict(
+    input_data=[d1],
+)
+
+# Serialize data into file:
+json.dump(data, open("input.json", 'w'))

examples/onnx/exp/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.5801457762718201, 0.6019012331962585, 0.8695418238639832, 0.17170941829681396, 0.500616729259491, 0.353726327419281, 0.6726185083389282, 0.5936906337738037]]}

examples/onnx/exp/network.onnx

@@ -0,0 +1,14 @@
+pytorch2.2.2:o
+
+inputoutput/Exp"Exp
+main_graphZ!
+input
+

+
+batch_size
+b"
+output
+

+
+batch_size
+B

examples/onnx/general_exp/gen.py

@@ -0,0 +1,41 @@
+from torch import nn
+import torch
+import json
+import numpy as np
+
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+
+    def forward(self, x):          
+        m = 10**x
+        return m 
+
+
+circuit = MyModel()
+
+x = torch.empty(1, 8).uniform_(0, 1)
+
+out = circuit(x)
+
+print(out)
+
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=17,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  input_names=['input'],   # the model's input names
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
+                                'output': {0: 'batch_size'}})
+
+
+d1 = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data = dict(
+    input_data=[d1],
+)
+
+# Serialize data into file:
+json.dump(data, open("input.json", 'w'))

examples/onnx/general_exp/input.json

@@ -0,0 +1 @@
+{"input_data": [[0.9837989807128906, 0.026381194591522217, 0.3403851389884949, 0.14531707763671875, 0.24652725458145142, 0.7945117354393005, 0.4076554775238037, 0.23064672946929932]]}

examples/onnx/log/gen.py

@@ -0,0 +1,42 @@
+from torch import nn
+import torch
+import json
+import numpy as np
+
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+
+    def forward(self, x):          
+        m = torch.log(x)
+        
+        return m 
+
+
+circuit = MyModel()
+
+x = torch.empty(1, 8).uniform_(0, 3)
+
+out = circuit(x)
+
+print(out)
+
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=17,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  input_names=['input'],   # the model's input names
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
+                                'output': {0: 'batch_size'}})
+
+
+d1 = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data = dict(
+    input_data=[d1],
+)
+
+# Serialize data into file:
+json.dump(data, open("input.json", 'w'))

examples/onnx/log/input.json

@@ -0,0 +1 @@
+{"input_data": [[1.9252371788024902, 1.8418371677398682, 0.8400403261184692, 2.083845853805542, 0.9760497808456421, 0.6940176486968994, 0.015579521656036377, 2.2689192295074463]]}

examples/onnx/log/network.onnx

@@ -0,0 +1,14 @@
+pytorch2.2.2:o
+
+inputoutput/Log"Log
+main_graphZ!
+input
+

+
+batch_size
+b"
+output
+

+
+batch_size
+B

examples/onnx/rounding_ops/input.json

@@ -1 +1,148 @@
-{"input_shapes": [[3, 2, 3], [3, 2, 3], [3, 2, 3], [3, 2, 3]], "input_data": [[0.6261028051376343, 0.49872446060180664, -0.04514765739440918, 0.5936200618743896, 0.9271858930587769, 0.6688600778579712, -0.20331168174743652, -0.7016235589981079, 0.025863051414489746, -0.19426143169403076, 0.9827852249145508, 0.4897397756576538, 0.2992602586746216, 0.7011144161224365, 0.9278832674026489, 0.5943725109100342, -0.573331356048584, 0.3675816059112549], [0.7803324460983276, -0.9616303443908691, 0.6070173978805542, -0.028337717056274414, -0.5080242156982422, -0.9280107021331787, 0.6150380373001099, 0.3865993022918701, -0.43668973445892334, 0.17152702808380127, 0.5144252777099609, -0.28881049156188965, 0.8932310342788696, 0.059034109115600586, 0.6865451335906982, 0.009820222854614258, 0.23011493682861328, -0.9492779970169067], [-0.21352827548980713, -0.16015326976776123, -0.38964390754699707, 0.13464701175689697, -0.8814496994018555, 0.5037975311279297, -0.804405927658081, 0.9858957529067993, 0.19567716121673584, 0.9777265787124634, 0.6151977777481079, 0.568595290184021, 0.10584986209869385, -0.8975653648376465, 0.6235959529876709, -0.547879695892334, 0.9289869070053101, 0.7567293643951416]], "output_data": [[1.0, 0.0, -0.0, 1.0, 1.0, 1.0, -0.0, -1.0, 0.0, -0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, -1.0, 0.0], [0.0, -1.0, 0.0, -1.0, -1.0, -1.0, 0.0, 0.0, -1.0, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0], [-0.0, -0.0, -0.0, 1.0, -0.0, 1.0, -0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.0, 1.0, -0.0, 1.0, 1.0]]}
+{
+    "input_shapes": [
+        [
+            3,
+            2,
+            3
+        ],
+        [
+            3,
+            2,
+            3
+        ],
+        [
+            3,
+            2,
+            3
+        ],
+        [
+            3,
+            2,
+            3
+        ]
+    ],
+    "input_data": [
+        [
+            0.5,
+            1.5,
+            -0.04514765739440918,
+            0.5936200618743896,
+            0.9271858930587769,
+            0.6688600778579712,
+            -0.20331168174743652,
+            -0.7016235589981079,
+            0.025863051414489746,
+            -0.19426143169403076,
+            0.9827852249145508,
+            0.4897397756576538,
+            -1.5,
+            -0.5,
+            0.9278832674026489,
+            0.5943725109100342,
+            -0.573331356048584,
+            0.3675816059112549
+        ],
+        [
+            0.7803324460983276,
+            -0.9616303443908691,
+            0.6070173978805542,
+            -0.028337717056274414,
+            -0.5080242156982422,
+            -0.9280107021331787,
+            0.6150380373001099,
+            0.3865993022918701,
+            -0.43668973445892334,
+            0.17152702808380127,
+            0.5144252777099609,
+            -0.28881049156188965,
+            0.8932310342788696,
+            0.059034109115600586,
+            0.6865451335906982,
+            0.009820222854614258,
+            0.23011493682861328,
+            -0.9492779970169067
+        ],
+        [
+            -0.21352827548980713,
+            -0.16015326976776123,
+            -0.38964390754699707,
+            0.13464701175689697,
+            -0.8814496994018555,
+            0.5037975311279297,
+            -0.804405927658081,
+            0.9858957529067993,
+            0.19567716121673584,
+            0.9777265787124634,
+            0.6151977777481079,
+            0.568595290184021,
+            0.10584986209869385,
+            -0.8975653648376465,
+            0.6235959529876709,
+            -0.547879695892334,
+            0.9289869070053101,
+            0.7567293643951416
+        ]
+    ],
+    "output_data": [
+        [
+            1.0,
+            0.0,
+            -0.0,
+            1.0,
+            1.0,
+            1.0,
+            -0.0,
+            -1.0,
+            0.0,
+            -0.0,
+            1.0,
+            0.0,
+            0.0,
+            1.0,
+            1.0,
+            1.0,
+            -1.0,
+            0.0
+        ],
+        [
+            0.0,
+            -1.0,
+            0.0,
+            -1.0,
+            -1.0,
+            -1.0,
+            0.0,
+            0.0,
+            -1.0,
+            0.0,
+            0.0,
+            -1.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            -1.0
+        ],
+        [
+            -0.0,
+            -0.0,
+            -0.0,
+            1.0,
+            -0.0,
+            1.0,
+            -0.0,
+            1.0,
+            1.0,
+            1.0,
+            1.0,
+            1.0,
+            1.0,
+            -0.0,
+            1.0,
+            -0.0,
+            1.0,
+            1.0
+        ]
+    ]
+}

src/bindings/python.rs

@@ -180,9 +180,6 @@ struct PyRunArgs {
     /// list[tuple[str, int]]: Hand-written parser for graph variables, eg. batch_size=1
     pub variables: Vec<(String, usize)>,
     #[pyo3(get, set)]
-    /// bool: Rebase the scale using lookup table for division instead of using a range check
-    pub div_rebasing: bool,
-    #[pyo3(get, set)]
     /// bool: Should constants with 0.0 fraction be rebased to scale 0
     pub rebase_frac_zero_constants: bool,
     #[pyo3(get, set)]
@@ -197,6 +194,9 @@ struct PyRunArgs {
     /// int: The number of legs used for decomposition
     #[pyo3(get, set)]
     pub decomp_legs: usize,
+    /// bool: Should the circuit use unbounded lookups for log
+    #[pyo3(get, set)]
+    pub bounded_log_lookup: bool,
 }
 
 /// default instantiation of PyRunArgs
@@ -212,6 +212,7 @@ impl PyRunArgs {
 impl From<PyRunArgs> for RunArgs {
     fn from(py_run_args: PyRunArgs) -> Self {
         RunArgs {
+            bounded_log_lookup: py_run_args.bounded_log_lookup,
             tolerance: Tolerance::from(py_run_args.tolerance),
             input_scale: py_run_args.input_scale,
             param_scale: py_run_args.param_scale,
@@ -223,7 +224,6 @@ impl From<PyRunArgs> for RunArgs {
             output_visibility: py_run_args.output_visibility,
             param_visibility: py_run_args.param_visibility,
             variables: py_run_args.variables,
-            div_rebasing: py_run_args.div_rebasing,
             rebase_frac_zero_constants: py_run_args.rebase_frac_zero_constants,
             check_mode: py_run_args.check_mode,
             commitment: Some(py_run_args.commitment.into()),
@@ -236,6 +236,7 @@ impl From<PyRunArgs> for RunArgs {
 impl Into<PyRunArgs> for RunArgs {
     fn into(self) -> PyRunArgs {
         PyRunArgs {
+            bounded_log_lookup: self.bounded_log_lookup,
             tolerance: self.tolerance.val,
             input_scale: self.input_scale,
             param_scale: self.param_scale,
@@ -247,7 +248,6 @@ impl Into<PyRunArgs> for RunArgs {
             output_visibility: self.output_visibility,
             param_visibility: self.param_visibility,
             variables: self.variables,
-            div_rebasing: self.div_rebasing,
             rebase_frac_zero_constants: self.rebase_frac_zero_constants,
             check_mode: self.check_mode,
             commitment: self.commitment.into(),
@@ -873,8 +873,6 @@ fn gen_settings(
 /// max_logrows: int
 ///     Optional max logrows to use for calibration
 ///
-/// only_range_check_rebase: bool
-///     Check ranges when rebasing
 ///
 /// Returns
 /// -------
@@ -889,7 +887,6 @@ fn gen_settings(
     scales = None,
     scale_rebase_multiplier = DEFAULT_SCALE_REBASE_MULTIPLIERS.split(",").map(|x| x.parse().unwrap()).collect(),
     max_logrows = None,
-    only_range_check_rebase = DEFAULT_ONLY_RANGE_CHECK_REBASE.parse().unwrap(),
 ))]
 fn calibrate_settings(
     py: Python,
@@ -901,7 +898,6 @@ fn calibrate_settings(
     scales: Option<Vec<crate::Scale>>,
     scale_rebase_multiplier: Vec<u32>,
     max_logrows: Option<u32>,
-    only_range_check_rebase: bool,
 ) -> PyResult<Bound<'_, PyAny>> {
     pyo3_asyncio::tokio::future_into_py(py, async move {
         crate::execute::calibrate(
@@ -912,7 +908,6 @@ fn calibrate_settings(
             lookup_safety_margin,
             scales,
             scale_rebase_multiplier,
-            only_range_check_rebase,
             max_logrows,
         )
         .await

src/circuit/ops/hybrid.rs

@@ -13,6 +13,20 @@ use serde::{Deserialize, Serialize};
 /// An enum representing the operations that consist of both lookups and arithmetic operations.
 #[derive(Clone, Debug, Serialize, Deserialize)]
 pub enum HybridOp {
+    Ln {
+        scale: utils::F32,
+    },
+    Rsqrt {
+        input_scale: utils::F32,
+        output_scale: utils::F32,
+    },
+    Sqrt {
+        scale: utils::F32,
+    },
+    RoundHalfToEven {
+        scale: utils::F32,
+        legs: usize,
+    },
     Ceil {
         scale: utils::F32,
         legs: usize,
@@ -31,7 +45,6 @@ pub enum HybridOp {
     },
     Div {
         denom: utils::F32,
-        use_range_check_for_int: bool,
     },
     ReduceMax {
         axes: Vec<usize>,
@@ -108,11 +121,24 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
 
     fn as_string(&self) -> String {
         match self {
+            HybridOp::Rsqrt {
+                input_scale,
+                output_scale,
+            } => format!(
+                "RSQRT (input_scale={}, output_scale={})",
+                input_scale, output_scale
+            ),
+            HybridOp::Sqrt { scale } => format!("SQRT(scale={})", scale),
+            HybridOp::Ln { scale } => format!("LN(scale={})", scale),
+            HybridOp::RoundHalfToEven { scale, legs } => {
+                format!("ROUND_HALF_TO_EVEN(scale={}, legs={})", scale, legs)
+            }
             HybridOp::Ceil { scale, legs } => format!("CEIL(scale={}, legs={})", scale, legs),
             HybridOp::Floor { scale, legs } => format!("FLOOR(scale={}, legs={})", scale, legs),
             HybridOp::Round { scale, legs } => format!("ROUND(scale={}, legs={})", scale, legs),
-            HybridOp::Max => format!("MAX"),
-            HybridOp::Min => format!("MIN"),
+
+            HybridOp::Max => "MAX".to_string(),
+            HybridOp::Min => "MIN".to_string(),
             HybridOp::Recip {
                 input_scale,
                 output_scale,
@@ -120,13 +146,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                 "RECIP (input_scale={}, output_scale={})",
                 input_scale, output_scale
             ),
-            HybridOp::Div {
-                denom,
-                use_range_check_for_int,
-            } => format!(
-                "DIV (denom={}, use_range_check_for_int={})",
-                denom, use_range_check_for_int
-            ),
+            HybridOp::Div { denom } => format!("DIV (denom={})", denom),
             HybridOp::SumPool {
                 padding,
                 stride,
@@ -181,6 +201,23 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
         values: &[ValTensor<F>],
     ) -> Result<Option<ValTensor<F>>, CircuitError> {
         Ok(Some(match self {
+            HybridOp::Rsqrt {
+                input_scale,
+                output_scale,
+            } => layouts::rsqrt(
+                config,
+                region,
+                values[..].try_into()?,
+                *input_scale,
+                *output_scale,
+            )?,
+            HybridOp::Sqrt { scale } => {
+                layouts::sqrt(config, region, values[..].try_into()?, *scale)?
+            }
+            HybridOp::Ln { scale } => layouts::ln(config, region, values[..].try_into()?, *scale)?,
+            HybridOp::RoundHalfToEven { scale, legs } => {
+                layouts::round_half_to_even(config, region, values[..].try_into()?, *scale, *legs)?
+            }
             HybridOp::Ceil { scale, legs } => {
                 layouts::ceil(config, region, values[..].try_into()?, *scale, *legs)?
             }
@@ -216,13 +253,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
                 integer_rep_to_felt(input_scale.0 as i128),
                 integer_rep_to_felt(output_scale.0 as i128),
             )?,
-            HybridOp::Div {
-                denom,
-                use_range_check_for_int,
-                ..
-            } => {
-                if denom.0.fract() == 0.0 && *use_range_check_for_int {
-                    layouts::loop_div(
+            HybridOp::Div { denom, .. } => {
+                if denom.0.fract() == 0.0 {
+                    layouts::div(
                         config,
                         region,
                         values[..].try_into()?,
@@ -313,9 +346,18 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
             | HybridOp::ReduceArgMax { .. }
             | HybridOp::OneHot { .. }
             | HybridOp::ReduceArgMin { .. } => 0,
-            HybridOp::Softmax { output_scale, .. } | HybridOp::Recip { output_scale, .. } => {
+
+            HybridOp::Recip { output_scale, .. } | HybridOp::Rsqrt { output_scale, .. } => {
                 multiplier_to_scale(output_scale.0 as f64)
             }
+            HybridOp::Softmax {
+                output_scale,
+                input_scale,
+                ..
+            } => multiplier_to_scale((output_scale.0 * input_scale.0) as f64),
+            HybridOp::Ln {
+                scale: output_scale,
+            } => 4 * multiplier_to_scale(output_scale.0 as f64),
             _ => in_scales[0],
         };
         Ok(scale)

src/circuit/ops/layouts.rs

@@ -1,5 +1,6 @@
 use std::{
     collections::{HashMap, HashSet},
+    f64::consts::E,
     ops::Range,
 };
 
@@ -29,41 +30,96 @@ use crate::{
 use super::*;
 use crate::circuit::ops::lookup::LookupOp;
 
-/// Same as div but splits the division into N parts
-pub(crate) fn loop_div<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+/// Calculate the L1 distance between two tensors.
+/// ```
+/// use ezkl::tensor::Tensor;
+/// use ezkl::fieldutils::IntegerRep;
+/// use ezkl::circuit::ops::layouts::l1_distance;
+/// use halo2curves::bn256::Fr as Fp;
+/// use ezkl::circuit::region::RegionCtx;
+/// use ezkl::circuit::region::RegionSettings;
+/// use ezkl::circuit::BaseConfig;
+/// use ezkl::tensor::ValTensor;
+/// let dummy_config = BaseConfig::dummy(12, 2);
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
+///    Some(&[1, 2, 3, 2, 3, 4, 3, 4, 5]),
+/// &[3, 3],
+/// ).unwrap());
+/// let k = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
+///   Some(&[1, 2, 3, 1, 2, 3, 1, 2, 3]),
+/// &[3, 3],
+/// ).unwrap());
+/// let result = l1_distance::<Fp>(&dummy_config, &mut dummy_region, &[x, k]).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(Some(&[0, 0, 0, 1, 1, 1, 2, 2, 2]), &[3, 3]).unwrap();
+/// assert_eq!(result.int_evals().unwrap(), expected);
+/// ```
+pub fn l1_distance<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
-    value: &[ValTensor<F>; 1],
-    divisor: F,
+    values: &[ValTensor<F>; 2],
 ) -> Result<ValTensor<F>, CircuitError> {
-    if divisor == F::ONE {
-        return Ok(value[0].clone());
-    }
+    let diff = pairwise(config, region, values, BaseOp::Sub)?;
+    let abs_diff = abs(config, region, &[diff])?;
 
-    // if integer val is divisible by 2, we can use a faster method and div > F::S
-    let mut divisor = divisor;
-    let mut num_parts = 1;
+    Ok(abs_diff)
+}
 
-    while felt_to_integer_rep(divisor) % 2 == 0
-        && felt_to_integer_rep(divisor) > (2_i128.pow(F::S - 4))
-    {
-        divisor = integer_rep_to_felt(felt_to_integer_rep(divisor) / 2);
-        num_parts += 1;
-    }
+/// Determines if from a set of 3 tensors the 1st is closest to a reference tensor.
+/// should only be used in the context of a monotonic function like the product used in the division, recipe, and sqrt arguments;
+/// or the increasing powers of 2 in the ln argument. Which is used to construct a convex error function.
+fn optimum_convex_function<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    x: &ValTensor<F>,
+    f: impl Fn(&BaseConfig<F>, &mut RegionCtx<F>, &ValTensor<F>) -> Result<ValTensor<F>, CircuitError>,
+) -> Result<(), CircuitError> {
+    let one = create_constant_tensor(F::from(1), 1);
 
-    let output = div(config, region, value, divisor)?;
-    if num_parts == 1 {
-        return Ok(output);
-    }
+    let f_x = f(config, region, x)?;
 
-    let divisor_int = 2_i128.pow(num_parts - 1);
-    let divisor_felt = integer_rep_to_felt(divisor_int);
-    if divisor_int <= 2_i128.pow(F::S - 3) {
-        div(config, region, &[output], divisor_felt)
-    } else {
-        // keep splitting the divisor until it satisfies the condition
-        loop_div(config, region, &[output], divisor_felt)
-    }
+    let x_plus_1 = pairwise(config, region, &[x.clone(), one.clone()], BaseOp::Add)?;
+    let f_x_plus_1 = f(config, region, &x_plus_1)?;
+
+    let x_minus_1 = pairwise(config, region, &[x.clone(), one.clone()], BaseOp::Sub)?;
+    let f_x_minus_1 = f(config, region, &x_minus_1)?;
+
+    // because the function is convex, the result should be the minimum of the three
+    // not that we offset the x by 1 to get the next value
+    // f(x) <= f(x+1) and f(x) <= f(x-1)
+    // the result is 1 if the function is optimal solely because of the convexity of the function
+    // the distances can be equal but this is only possible if f(x) and f(x+1) are both optimal (or f(x) and f(x-1)).
+    let f_x_is_opt_rhs = less_equal(config, region, &[f_x.clone(), f_x_plus_1.clone()])?;
+    let f_x_is_opt_lhs = less_equal(config, region, &[f_x.clone(), f_x_minus_1.clone()])?;
+
+    let is_opt = and(config, region, &[f_x_is_opt_lhs, f_x_is_opt_rhs])?;
+
+    let mut comparison_unit = create_constant_tensor(F::ONE, is_opt.len());
+    comparison_unit.reshape(is_opt.dims())?;
+
+    // assert that the result is 1
+    enforce_equality(config, region, &[is_opt, comparison_unit])?;
+
+    Ok(())
+}
+
+/// Err is less than some constant
+pub fn diff_less_than<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    values: &[ValTensor<F>; 2],
+    constant: F,
+) -> Result<(), CircuitError> {
+    let distance = l1_distance(config, region, values)?;
+
+    let constant = create_constant_tensor(constant, 1);
+    let is_less = less(config, region, &[distance.clone(), constant.clone()])?;
+
+    // assert the result is 1
+    let comparison_unit = create_constant_tensor(F::ONE, is_less.len());
+    enforce_equality(config, region, &[is_less, comparison_unit])?;
+
+    Ok(())
 }
 
 /// Div accumulated layout
@@ -80,13 +136,8 @@ pub(crate) fn div<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     let input = value[0].clone();
     let input_dims = input.dims();
 
-    let range_check_bracket = felt_to_integer_rep(div) / 2;
-
     let divisor = create_constant_tensor(div, 1);
 
-    let divisor = region.assign(&config.custom_gates.inputs[1], &divisor)?;
-    region.increment(divisor.len());
-
     let is_assigned = !input.any_unknowns()? && !divisor.any_unknowns()?;
 
     let mut claimed_output: ValTensor<F> = if is_assigned {
@@ -117,19 +168,7 @@ pub(crate) fn div<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
         BaseOp::Mult,
     )?;
 
-    let diff_with_input = pairwise(
-        config,
-        region,
-        &[product.clone(), input.clone()],
-        BaseOp::Sub,
-    )?;
-
-    range_check(
-        config,
-        region,
-        &[diff_with_input],
-        &(-range_check_bracket, range_check_bracket),
-    )?;
+    diff_less_than(config, region, &[input.clone(), product.clone()], div)?;
 
     Ok(claimed_output)
 }
@@ -145,19 +184,7 @@ pub(crate) fn recip<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     let input = value[0].clone();
     let input_dims = input.dims();
 
-    let integer_input_scale = felt_to_integer_rep(input_scale);
-    let integer_output_scale = felt_to_integer_rep(output_scale);
-
-    // range_check_bracket is min of input_scale * output_scale and 2^F::S - 3
-    let range_check_len = std::cmp::min(integer_output_scale, 2_i128.pow(F::S - 4));
-
-    let input_scale_ratio = if range_check_len > 0 {
-        integer_rep_to_felt(integer_input_scale * integer_output_scale / range_check_len)
-    } else {
-        F::ONE
-    };
-
-    let range_check_bracket = range_check_len / 2;
+    let unit_scale = create_constant_tensor(output_scale * input_scale, 1);
 
     let is_assigned = !input.any_unknowns()?;
 
@@ -183,25 +210,22 @@ pub(crate) fn recip<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     let claimed_output = region.assign(&config.custom_gates.output, &claimed_output)?;
     region.increment(claimed_output.len());
 
-    // this is now of scale 2 * scale
-    let product = pairwise(
-        config,
-        region,
-        &[claimed_output.clone(), input.clone()],
-        BaseOp::Mult,
-    )?;
-
     // divide by input_scale
-    let rebased_div = loop_div(config, region, &[product], input_scale_ratio)?;
-
     let zero_inverse_val =
         tensor::ops::nonlinearities::zero_recip(felt_to_integer_rep(output_scale) as f64)[0];
     let zero_inverse = create_constant_tensor(integer_rep_to_felt(zero_inverse_val), 1);
 
     let equal_zero_mask = equals_zero(config, region, &[input.clone()])?;
-
+    let not_equal_zero_mask = not(config, region, &[equal_zero_mask.clone()])?;
     let equal_inverse_mask = equals(config, region, &[claimed_output.clone(), zero_inverse])?;
 
+    let masked_unit_scale = pairwise(
+        config,
+        region,
+        &[unit_scale.clone(), not_equal_zero_mask.clone()],
+        BaseOp::Mult,
+    )?;
+
     // assert the two masks are equal
     enforce_equality(
         config,
@@ -209,24 +233,135 @@ pub(crate) fn recip<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
         &[equal_zero_mask.clone(), equal_inverse_mask],
     )?;
 
-    let unit_scale = create_constant_tensor(integer_rep_to_felt(range_check_len), 1);
+    let err_func = |config: &BaseConfig<F>,
+                    region: &mut RegionCtx<F>,
+                    x: &ValTensor<F>|
+     -> Result<ValTensor<F>, CircuitError> {
+        let product = pairwise(config, region, &[x.clone(), input.clone()], BaseOp::Mult)?;
 
-    let unit_mask = pairwise(config, region, &[equal_zero_mask, unit_scale], BaseOp::Mult)?;
+        let distance = l1_distance(
+            config,
+            region,
+            &[product.clone(), masked_unit_scale.clone()],
+        )?;
+        Ok(distance)
+    };
 
-    // now add the unit mask to the rebased_div
-    let rebased_offset_div = pairwise(config, region, &[rebased_div, unit_mask], BaseOp::Add)?;
-
-    // at most the error should be in the original unit scale's range
-    range_check(
-        config,
-        region,
-        &[rebased_offset_div],
-        &(range_check_bracket, 3 * range_check_bracket),
-    )?;
+    optimum_convex_function(config, region, &claimed_output, err_func)?;
 
     Ok(claimed_output)
 }
 
+/// Square root accumulated layout
+/// # Example
+/// ```
+/// use ezkl::tensor::Tensor;
+/// use ezkl::fieldutils::IntegerRep;
+/// use ezkl::circuit::ops::layouts::sqrt;
+/// use halo2curves::bn256::Fr as Fp;
+/// use ezkl::circuit::region::RegionCtx;
+/// use ezkl::circuit::region::RegionSettings;
+/// use ezkl::circuit::BaseConfig;
+/// use ezkl::tensor::ValTensor;
+/// let dummy_config = BaseConfig::dummy(12, 2);
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
+///     Some(&[1, 2, 3, 2, 3, 4, 3, 4, 9]),
+///    &[3, 3],
+/// ).unwrap());
+/// let result = sqrt::<Fp>(&dummy_config, &mut dummy_region, &[x], 1.0.into()).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(Some(&[1, 1, 2, 1, 2, 2, 2, 2, 3]), &[3, 3]).unwrap();
+/// assert_eq!(result.int_evals().unwrap(), expected);
+/// ```
+pub fn sqrt<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    value: &[ValTensor<F>; 1],
+    input_scale: utils::F32,
+) -> Result<ValTensor<F>, CircuitError> {
+    let input = value[0].clone();
+    let input_dims = input.dims();
+
+    let unit_scale = create_constant_tensor(integer_rep_to_felt(input_scale.0 as IntegerRep), 1);
+
+    let is_assigned = !input.any_unknowns()?;
+
+    let mut claimed_output: ValTensor<F> = if is_assigned {
+        let input_evals = input.int_evals()?;
+        tensor::ops::nonlinearities::sqrt(&input_evals, input_scale.0 as f64)
+            .par_iter()
+            .map(|x| Value::known(integer_rep_to_felt(*x)))
+            .collect::<Tensor<Value<F>>>()
+            .into()
+    } else {
+        Tensor::new(
+            Some(&vec![Value::<F>::unknown(); input.len()]),
+            &[input.len()],
+        )?
+        .into()
+    };
+    claimed_output.reshape(input_dims)?;
+    let claimed_output = region.assign(&config.custom_gates.output, &claimed_output)?;
+    region.increment(claimed_output.len());
+
+    // force the output to be positive or zero
+    let claimed_output = abs(config, region, &[claimed_output.clone()])?;
+
+    // rescaled input
+    let rescaled_input = pairwise(config, region, &[input.clone(), unit_scale], BaseOp::Mult)?;
+
+    let err_func = |config: &BaseConfig<F>,
+                    region: &mut RegionCtx<F>,
+                    x: &ValTensor<F>|
+     -> Result<ValTensor<F>, CircuitError> {
+        let product = pairwise(config, region, &[x.clone(), x.clone()], BaseOp::Mult)?;
+        let distance = l1_distance(config, region, &[product.clone(), rescaled_input.clone()])?;
+        Ok(distance)
+    };
+
+    optimum_convex_function(config, region, &claimed_output, err_func)?;
+
+    Ok(claimed_output)
+}
+
+/// Reciprocal square root accumulated layout
+/// # Example
+/// ```
+/// use ezkl::tensor::Tensor;
+/// use ezkl::fieldutils::IntegerRep;
+/// use ezkl::circuit::ops::layouts::rsqrt;
+/// use halo2curves::bn256::Fr as Fp;
+/// use ezkl::circuit::region::RegionCtx;
+/// use ezkl::circuit::region::RegionSettings;
+/// use ezkl::circuit::BaseConfig;
+/// use ezkl::tensor::ValTensor;
+/// let dummy_config = BaseConfig::dummy(12, 2);
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
+///    Some(&[1, 2, 3, 2, 3, 4, 3, 4, 5]),
+/// &[3, 3],
+/// ).unwrap());
+/// let result = rsqrt::<Fp>(&dummy_config, &mut dummy_region, &[x], 1.0.into(), 1.0.into()).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(Some(&[1, 1, 1, 1, 1, 1, 1, 1, 1]), &[3, 3]).unwrap();
+/// assert_eq!(result.int_evals().unwrap(), expected);
+/// ```
+pub fn rsqrt<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    value: &[ValTensor<F>; 1],
+    input_scale: utils::F32,
+    output_scale: utils::F32,
+) -> Result<ValTensor<F>, CircuitError> {
+    let sqrt = sqrt(config, region, value, input_scale)?;
+
+    let felt_output_scale = integer_rep_to_felt(output_scale.0 as IntegerRep);
+    let felt_input_scale = integer_rep_to_felt(input_scale.0 as IntegerRep);
+
+    let recip = recip(config, region, &[sqrt], felt_input_scale, felt_output_scale)?;
+
+    Ok(recip)
+}
+
 /// Dot product of two tensors.
 /// ```
 /// use ezkl::tensor::Tensor;
@@ -1805,6 +1940,10 @@ pub fn sum<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     region: &mut RegionCtx<F>,
     values: &[ValTensor<F>; 1],
 ) -> Result<ValTensor<F>, CircuitError> {
+    if values[0].len() == 1 {
+        return Ok(values[0].clone());
+    }
+
     region.flush()?;
     // time this entire function run
     let global_start = instant::Instant::now();
@@ -3102,7 +3241,7 @@ pub fn sumpool<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     last_elem.reshape(&[&[batch_size, image_channels], shape].concat())?;
 
     if normalized {
-        last_elem = loop_div(config, region, &[last_elem], F::from(kernel_len as u64))?;
+        last_elem = div(config, region, &[last_elem], F::from(kernel_len as u64))?;
     }
     Ok(last_elem)
 }
@@ -4507,6 +4646,293 @@ pub fn ceil<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     )
 }
 
+/// integer ln layout
+/// # Arguments
+/// * `config` - BaseConfig
+/// * `region` - RegionCtx
+/// * `values` - &[ValTensor<F>; 1]
+/// * `scale` - utils::F32
+/// # Returns
+/// * ValTensor<F>
+/// # Example
+///
+/// ```
+/// use ezkl::tensor::Tensor;
+/// use ezkl::fieldutils::IntegerRep;
+/// use ezkl::circuit::ops::layouts::ln;
+/// use ezkl::tensor::val::ValTensor;
+/// use halo2curves::bn256::Fr as Fp;
+/// use ezkl::circuit::region::RegionCtx;
+/// use ezkl::circuit::region::RegionSettings;
+/// use ezkl::circuit::BaseConfig;
+/// let dummy_config = BaseConfig::dummy(12, 2);
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
+/// Some(&[3, 2, 3, 1]),
+/// &[1, 1, 2, 2],
+/// ).unwrap());
+///
+/// let result = ln::<Fp>(&dummy_config, &mut dummy_region, &[x], 2.0.into()).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(Some(&[4, 0, 4, -8]), &[1, 1, 2, 2]).unwrap();
+/// assert_eq!(result.int_evals().unwrap(), expected);
+///
+/// ```
+pub fn ln<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    values: &[ValTensor<F>; 1],
+    scale: utils::F32,
+) -> Result<ValTensor<F>, CircuitError> {
+    // first generate the claimed val
+
+    let mut input = values[0].clone();
+    let scale_as_felt = integer_rep_to_felt(scale.0.round() as IntegerRep);
+
+    let triple_scaled_as_felt_tensor =
+        create_constant_tensor(scale_as_felt * scale_as_felt * scale_as_felt, 1);
+
+    // natural ln is log2(x) * ln(2)
+    let ln2 = utils::F32::from(2.0_f32.ln());
+    // now create a constant tensor for ln2 with scale
+    let ln2_tensor: ValTensor<F> = create_constant_tensor(
+        integer_rep_to_felt((ln2.0 * scale.0).round() as IntegerRep),
+        1,
+    );
+    let unit = create_constant_tensor(integer_rep_to_felt(1), 1);
+    let negative_one = create_constant_tensor(integer_rep_to_felt(-1), 1);
+
+    // 2. assign the image
+    if !input.all_prev_assigned() {
+        input = region.assign(&config.custom_gates.inputs[0], &input)?;
+        // don't need to increment because the claimed output is assigned to output and incremented accordingly
+    }
+
+    let is_assigned = !input.any_unknowns()?;
+
+    let mut claimed_output: ValTensor<F> = if is_assigned {
+        let input_evals = input.int_evals()?;
+        // returns an integer with the base 2 logarithm
+        tensor::ops::nonlinearities::ilog2(&input_evals.clone(), scale.0 as f64)
+            .par_iter()
+            .map(|x| Value::known(integer_rep_to_felt(*x)))
+            .collect::<Tensor<Value<F>>>()
+            .into()
+    } else {
+        Tensor::new(
+            Some(&vec![Value::<F>::unknown(); input.len()]),
+            &[input.len()],
+        )?
+        .into()
+    };
+    claimed_output.reshape(input.dims())?;
+    region.assign(&config.custom_gates.output, &claimed_output)?;
+    region.increment(claimed_output.len());
+
+    let pow2_of_claimed_output = nonlinearity(
+        config,
+        region,
+        &[claimed_output.clone()],
+        &LookupOp::PowersOfTwo { scale },
+    )?;
+
+    let num_bits = (std::mem::size_of::<IntegerRep>() * 8) as IntegerRep;
+
+    region.update_max_min_lookup_inputs_force(-num_bits, num_bits)?;
+
+    // now subtract 1 from the claimed output
+    let claimed_output_minus_one = pairwise(
+        config,
+        region,
+        &[claimed_output.clone(), unit.clone()],
+        BaseOp::Sub,
+    )?;
+
+    // now add 1 to the claimed output
+    let claimed_output_plus_one = pairwise(
+        config,
+        region,
+        &[claimed_output.clone(), unit.clone()],
+        BaseOp::Add,
+    )?;
+
+    // prior power of 2 is less than claimed output
+    let prior_pow2 = nonlinearity(
+        config,
+        region,
+        &[claimed_output_minus_one],
+        &LookupOp::PowersOfTwo { scale },
+    )?;
+
+    // next power of 2 is greater than claimed output
+    let next_pow2 = nonlinearity(
+        config,
+        region,
+        &[claimed_output_plus_one],
+        &LookupOp::PowersOfTwo { scale },
+    )?;
+
+    let distance_to_claimed = pairwise(
+        config,
+        region,
+        &[input.clone(), pow2_of_claimed_output.clone()],
+        BaseOp::Sub,
+    )?;
+
+    let abs_distance_to_claimed = abs(config, region, &[distance_to_claimed.clone()])?;
+
+    let abs_distance_to_next_pow2 =
+        l1_distance(config, region, &[input.clone(), next_pow2.clone()])?;
+
+    let abs_distance_to_prior_pow2 =
+        l1_distance(config, region, &[input.clone(), prior_pow2.clone()])?;
+
+    // because we round up this can be equal
+    let is_closest_to_0: ValTensor<F> = less(
+        config,
+        region,
+        &[
+            abs_distance_to_claimed.clone(),
+            abs_distance_to_next_pow2.clone(),
+        ],
+    )?;
+
+    let is_closest_to_1 = less(
+        config,
+        region,
+        &[
+            abs_distance_to_claimed.clone(),
+            abs_distance_to_prior_pow2.clone(),
+        ],
+    )?;
+
+    let is_closest = and(
+        config,
+        region,
+        &[is_closest_to_0.clone(), is_closest_to_1.clone()],
+    )?;
+
+    let mut comparison_unit = create_constant_tensor(integer_rep_to_felt(1), is_closest.len());
+    comparison_unit.reshape(is_closest.dims())?;
+    let assigned_unit = region.assign(&config.custom_gates.inputs[1], &comparison_unit)?;
+
+    enforce_equality(config, region, &[is_closest, assigned_unit])?;
+
+    // get a linear interpolation now
+
+    let sign_of_distance_to_claimed = sign(config, region, &[distance_to_claimed.clone()])?;
+    let sign_of_distance_to_claimed_is_negative = equals(
+        config,
+        region,
+        &[sign_of_distance_to_claimed.clone(), negative_one.clone()],
+    )?;
+
+    let sign_of_distance_to_claimed_is_positive = not(
+        config,
+        region,
+        &[sign_of_distance_to_claimed_is_negative.clone()],
+    )?;
+
+    let pow2_prior_to_claimed_distance = pairwise(
+        config,
+        region,
+        &[pow2_of_claimed_output.clone(), prior_pow2.clone()],
+        BaseOp::Sub,
+    )?;
+
+    let pow2_next_to_claimed_distance = pairwise(
+        config,
+        region,
+        &[next_pow2.clone(), pow2_of_claimed_output.clone()],
+        BaseOp::Sub,
+    )?;
+
+    let recip_pow2_prior_to_claimed_distance = recip(
+        config,
+        region,
+        &[pow2_prior_to_claimed_distance],
+        scale_as_felt,
+        scale_as_felt * scale_as_felt,
+    )?;
+
+    let interpolated_distance = pairwise(
+        config,
+        region,
+        &[
+            recip_pow2_prior_to_claimed_distance.clone(),
+            distance_to_claimed.clone(),
+        ],
+        BaseOp::Mult,
+    )?;
+
+    let gated_prior_interpolated_distance = pairwise(
+        config,
+        region,
+        &[
+            interpolated_distance.clone(),
+            sign_of_distance_to_claimed_is_negative.clone(),
+        ],
+        BaseOp::Mult,
+    )?;
+
+    let recip_next_to_claimed_distance = recip(
+        config,
+        region,
+        &[pow2_next_to_claimed_distance],
+        scale_as_felt,
+        scale_as_felt * scale_as_felt,
+    )?;
+
+    let interpolated_distance_next = pairwise(
+        config,
+        region,
+        &[
+            recip_next_to_claimed_distance.clone(),
+            distance_to_claimed.clone(),
+        ],
+        BaseOp::Mult,
+    )?;
+
+    let gated_next_interpolated_distance = pairwise(
+        config,
+        region,
+        &[
+            interpolated_distance_next.clone(),
+            sign_of_distance_to_claimed_is_positive.clone(),
+        ],
+        BaseOp::Mult,
+    )?;
+
+    let scaled_claimed_output = pairwise(
+        config,
+        region,
+        &[claimed_output.clone(), triple_scaled_as_felt_tensor],
+        BaseOp::Mult,
+    )?;
+
+    let claimed_output = pairwise(
+        config,
+        region,
+        &[
+            scaled_claimed_output.clone(),
+            gated_prior_interpolated_distance.clone(),
+        ],
+        BaseOp::Add,
+    )?;
+
+    let claimed_output = pairwise(
+        config,
+        region,
+        &[
+            claimed_output.clone(),
+            gated_next_interpolated_distance.clone(),
+        ],
+        BaseOp::Add,
+    )?;
+
+    // now multiply the claimed output by ln2
+    pairwise(config, region, &[claimed_output, ln2_tensor], BaseOp::Mult)
+}
+
 /// round layout
 /// # Arguments
 /// * `config` - BaseConfig
@@ -4551,11 +4977,7 @@ pub fn round<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     let zero = ValType::Constant(F::ZERO);
 
     let one = create_constant_tensor(integer_rep_to_felt(1), 1);
-    let assigned_one = region.assign(&config.custom_gates.inputs[1], &one)?;
     let negative_one = create_constant_tensor(integer_rep_to_felt(-1), 1);
-    let assigned_negative_one = region.assign(&config.custom_gates.output, &negative_one)?;
-
-    region.increment(1);
 
     // if scale is not exactly divisible by 2 we warn
     if scale.0 % 2.0 != 0.0 {
@@ -4588,8 +5010,8 @@ pub fn round<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
             let last_elem = sliced_input.last()?;
 
             let sign = sliced_input.first()?;
-            let is_positive = equals(config, region, &[sign.clone(), assigned_one.clone()])?;
-            let is_negative = equals(config, region, &[sign, assigned_negative_one.clone()])?;
+            let is_positive = equals(config, region, &[sign.clone(), one.clone()])?;
+            let is_negative = equals(config, region, &[sign, negative_one.clone()])?;
 
             let is_greater_than_midway = greater_equal(
                 config,
@@ -4654,6 +5076,146 @@ pub fn round<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     )
 }
 
+/// round half to even layout
+/// # Arguments
+/// * `config` - BaseConfig
+/// * `region` - RegionCtx
+/// * `values` - &[ValTensor<F>; 1]
+/// * `scale` - utils::F32
+/// * `legs` - usize
+/// # Returns
+/// * ValTensor<F>
+/// # Example
+/// ```
+/// use ezkl::tensor::Tensor;
+/// use ezkl::fieldutils::IntegerRep;
+/// use ezkl::circuit::ops::layouts::round;
+/// use ezkl::tensor::val::ValTensor;
+/// use halo2curves::bn256::Fr as Fp;
+/// use ezkl::circuit::region::RegionCtx;
+/// use ezkl::circuit::region::RegionSettings;
+/// use ezkl::circuit::BaseConfig;
+/// let dummy_config = BaseConfig::dummy(12, 2);
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
+/// Some(&[3, -2, 3, 1]),
+/// &[1, 1, 2, 2],
+/// ).unwrap());
+/// let result = round::<Fp>(&dummy_config, &mut dummy_region, &[x], 4.0.into(), 2).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(Some(&[4, -4, 4, 0]), &[1, 1, 2, 2]).unwrap();
+/// assert_eq!(result.int_evals().unwrap(), expected);
+/// ```
+///
+pub fn round_half_to_even<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    values: &[ValTensor<F>; 1],
+    scale: utils::F32,
+    legs: usize,
+) -> Result<ValTensor<F>, CircuitError> {
+    // decompose with base scale and then set the last element to zero
+    let decomposition = decompose(config, region, values, &(scale.0 as usize), &legs)?;
+    // set the last element to zero and then recompose, we don't actually need to assign here
+    // as this will automatically be assigned in the recompose function and uses the constant caching of RegionCtx
+    let zero = ValType::Constant(F::ZERO);
+
+    // if scale is not exactly divisible by 2 we warn
+    if scale.0 % 2.0 != 0.0 {
+        log::warn!("Scale is not exactly divisible by 2.0, rounding may not be accurate");
+    }
+
+    let midway_point: ValTensor<F> = create_constant_tensor(
+        integer_rep_to_felt((scale.0 / 2.0).round() as IntegerRep),
+        1,
+    );
+
+    let dims = decomposition.dims().to_vec();
+    let first_dims = decomposition.dims().to_vec()[..decomposition.dims().len() - 1].to_vec();
+
+    let mut incremented_tensor = Tensor::new(None, &first_dims)?;
+
+    let cartesian_coord = first_dims
+        .iter()
+        .map(|x| 0..*x)
+        .multi_cartesian_product()
+        .collect::<Vec<_>>();
+
+    let inner_loop_function =
+        |i: usize, region: &mut RegionCtx<F>| -> Result<Tensor<ValType<F>>, CircuitError> {
+            let coord = cartesian_coord[i].clone();
+            let slice = coord.iter().map(|x| *x..*x + 1).collect::<Vec<_>>();
+            let mut sliced_input = decomposition.get_slice(&slice)?;
+            sliced_input.flatten();
+            let last_elem = sliced_input.last()?;
+
+            let penultimate_elem =
+                sliced_input.get_slice(&[sliced_input.len() - 2..sliced_input.len() - 1])?;
+
+            let is_equal_to_midway =
+                equals(config, region, &[last_elem.clone(), midway_point.clone()])?;
+            // penultimate_elem is equal to midway point and even, do nothing
+            let is_odd = nonlinearity(
+                config,
+                region,
+                &[penultimate_elem.clone()],
+                &LookupOp::IsOdd,
+            )?;
+
+            let is_odd_and_equal_to_midway = and(
+                config,
+                region,
+                &[is_odd.clone(), is_equal_to_midway.clone()],
+            )?;
+
+            let is_greater_than_midway =
+                greater(config, region, &[last_elem.clone(), midway_point.clone()])?;
+
+            // if the number is equal to midway point and odd increment, or if it is is_greater_than_midway
+            let is_odd_and_equal_to_midway_or_greater_than_midway = or(
+                config,
+                region,
+                &[
+                    is_odd_and_equal_to_midway.clone(),
+                    is_greater_than_midway.clone(),
+                ],
+            )?;
+
+            // increment the penultimate element
+            let incremented_elem = pairwise(
+                config,
+                region,
+                &[
+                    sliced_input.get_slice(&[sliced_input.len() - 2..sliced_input.len() - 1])?,
+                    is_odd_and_equal_to_midway_or_greater_than_midway.clone(),
+                ],
+                BaseOp::Add,
+            )?;
+
+            let mut inner_tensor = sliced_input.get_inner_tensor()?.clone();
+            inner_tensor[sliced_input.len() - 2] =
+                incremented_elem.get_inner_tensor()?.clone()[0].clone();
+
+            // set the last elem to zero
+            inner_tensor[sliced_input.len() - 1] = zero.clone();
+
+            Ok(inner_tensor.clone())
+        };
+
+    region.update_max_min_lookup_inputs_force(0, scale.0 as IntegerRep)?;
+
+    region.apply_in_loop(&mut incremented_tensor, inner_loop_function)?;
+
+    let mut incremented_tensor = incremented_tensor.combine()?;
+    incremented_tensor.reshape(&dims)?;
+
+    recompose(
+        config,
+        region,
+        &[incremented_tensor.into()],
+        &(scale.0 as usize),
+    )
+}
+
 pub(crate) fn recompose<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     config: &BaseConfig<F>,
     region: &mut RegionCtx<F>,
@@ -5019,11 +5581,8 @@ pub(crate) fn percent<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>
         input_felt_scale,
         output_felt_scale,
     )?;
-    // product of num * (1 / denom) = 2*output_scale
-    let percent = pairwise(config, region, &[input, inv_denom], BaseOp::Mult)?;
-
-    // rebase the percent to 2x the scale
-    loop_div(config, region, &[percent], input_felt_scale)
+    // product of num * (1 / denom) = input_scale * output_scale
+    pairwise(config, region, &[input, inv_denom], BaseOp::Mult)
 }
 
 /// Applies softmax
@@ -5047,7 +5606,7 @@ pub(crate) fn percent<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>
 /// ).unwrap());
 /// let result = softmax::<Fp>(&dummy_config, &mut dummy_region, &[x], 128.0.into(), (128.0 * 128.0).into()).unwrap();
 /// // doubles the scale of the input
-/// let expected = Tensor::<IntegerRep>::new(Some(&[2734, 2734, 2756, 2734, 2734, 2691]), &[2, 3]).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(Some(&[350012, 350012, 352768, 350012, 350012, 344500]), &[2, 3]).unwrap();
 /// assert_eq!(result.int_evals().unwrap(), expected);
 /// ```
 pub fn softmax<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
@@ -5066,7 +5625,10 @@ pub fn softmax<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
         config,
         region,
         &[sub],
-        &LookupOp::Exp { scale: input_scale },
+        &LookupOp::Exp {
+            scale: input_scale,
+            base: E.into(),
+        },
     )?;
 
     percent(config, region, &[ex.clone()], input_scale, output_scale)
@@ -5127,17 +5689,8 @@ pub fn range_check_percent<F: PrimeField + TensorType + PartialOrd + std::hash::
     let int_scale = scale.0 as IntegerRep;
     // felt scale
     let felt_scale = integer_rep_to_felt(int_scale);
-    // range check len capped at 2^(S-3) and make it divisible 2
-    let range_check_bracket = std::cmp::min(
-        utils::F32(scale.0),
-        utils::F32(2_f32.powf((F::S - 5) as f32)),
-    )
-    .0;
-
-    let range_check_bracket_int = range_check_bracket as IntegerRep;
-
     // input scale ratio we multiply by tol such that in the new scale range_check_len represents tol percent
-    let input_scale_ratio = ((scale.0.powf(2.0) / range_check_bracket) * tol) as IntegerRep / 2 * 2;
+    let input_scale_ratio = (scale.0 * tol) as IntegerRep / 2 * 2;
 
     let recip = recip(
         config,
@@ -5153,7 +5706,7 @@ pub fn range_check_percent<F: PrimeField + TensorType + PartialOrd + std::hash::
     let product = pairwise(config, region, &[diff, recip], BaseOp::Mult)?;
 
     log::debug!("product: {}", product.show());
-    let rebased_product = loop_div(
+    let rebased_product = div(
         config,
         region,
         &[product],
@@ -5162,10 +5715,5 @@ pub fn range_check_percent<F: PrimeField + TensorType + PartialOrd + std::hash::
     log::debug!("rebased_product: {}", rebased_product.show());
 
     // check that it is within the tolerance range
-    range_check(
-        config,
-        region,
-        &[rebased_product],
-        &(-range_check_bracket_int, range_check_bracket_int),
-    )
+    range_check(config, region, &[rebased_product], &(-int_scale, int_scale))
 }

src/circuit/ops/lookup.rs

@@ -4,7 +4,6 @@ use serde::{Deserialize, Serialize};
 use crate::{
     circuit::{layouts, table::Range, utils},
     fieldutils::{felt_to_integer_rep, integer_rep_to_felt, IntegerRep},
-    graph::multiplier_to_scale,
     tensor::{self, Tensor, TensorError, TensorType},
 };
 
@@ -16,13 +15,11 @@ use halo2curves::ff::PrimeField;
 #[derive(Clone, Debug, PartialEq, Eq, Hash, PartialOrd, Ord, Deserialize, Serialize)]
 pub enum LookupOp {
     Div { denom: utils::F32 },
-    Cast { scale: utils::F32 },
-    RoundHalfToEven { scale: utils::F32 },
-    Sqrt { scale: utils::F32 },
-    Rsqrt { scale: utils::F32 },
-    Sigmoid { scale: utils::F32 },
+    IsOdd,
+    PowersOfTwo { scale: utils::F32 },
     Ln { scale: utils::F32 },
-    Exp { scale: utils::F32 },
+    Sigmoid { scale: utils::F32 },
+    Exp { scale: utils::F32, base: utils::F32 },
     Cos { scale: utils::F32 },
     ACos { scale: utils::F32 },
     Cosh { scale: utils::F32 },
@@ -51,16 +48,14 @@ impl LookupOp {
     /// as path
     pub fn as_path(&self) -> String {
         match self {
-            LookupOp::RoundHalfToEven { scale } => format!("round_half_to_even_{}", scale),
             LookupOp::Pow { scale, a } => format!("pow_{}_{}", scale, a),
-            LookupOp::Div { denom } => format!("div_{}", denom),
-            LookupOp::Cast { scale } => format!("cast_{}", scale),
-            LookupOp::Sigmoid { scale } => format!("sigmoid_{}", scale),
-            LookupOp::Sqrt { scale } => format!("sqrt_{}", scale),
-            LookupOp::Rsqrt { scale } => format!("rsqrt_{}", scale),
-            LookupOp::Erf { scale } => format!("erf_{}", scale),
-            LookupOp::Exp { scale } => format!("exp_{}", scale),
             LookupOp::Ln { scale } => format!("ln_{}", scale),
+            LookupOp::PowersOfTwo { scale } => format!("pow2_{}", scale),
+            LookupOp::IsOdd => "is_odd".to_string(),
+            LookupOp::Div { denom } => format!("div_{}", denom),
+            LookupOp::Sigmoid { scale } => format!("sigmoid_{}", scale),
+            LookupOp::Erf { scale } => format!("erf_{}", scale),
+            LookupOp::Exp { scale, base } => format!("exp_{}_{}", scale, base),
             LookupOp::Cos { scale } => format!("cos_{}", scale),
             LookupOp::ACos { scale } => format!("acos_{}", scale),
             LookupOp::Cosh { scale } => format!("cosh_{}", scale),
@@ -85,36 +80,28 @@ impl LookupOp {
         let x = x[0].clone().map(|x| felt_to_integer_rep(x));
         let res =
             match &self {
-                LookupOp::RoundHalfToEven { scale } => Ok::<_, TensorError>(
-                    tensor::ops::nonlinearities::round_half_to_even(&x, scale.into()),
-                ),
+                LookupOp::Ln { scale } => {
+                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ln(&x, scale.into()))
+                }
+                LookupOp::PowersOfTwo { scale } => {
+                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ipow2(&x, scale.0.into()))
+                }
+                LookupOp::IsOdd => Ok::<_, TensorError>(tensor::ops::nonlinearities::is_odd(&x)),
                 LookupOp::Pow { scale, a } => Ok::<_, TensorError>(
                     tensor::ops::nonlinearities::pow(&x, scale.0.into(), a.0.into()),
                 ),
                 LookupOp::Div { denom } => Ok::<_, TensorError>(
                     tensor::ops::nonlinearities::const_div(&x, f32::from(*denom).into()),
                 ),
-                LookupOp::Cast { scale } => Ok::<_, TensorError>(
-                    tensor::ops::nonlinearities::const_div(&x, f32::from(*scale).into()),
-                ),
                 LookupOp::Sigmoid { scale } => {
                     Ok::<_, TensorError>(tensor::ops::nonlinearities::sigmoid(&x, scale.into()))
                 }
-                LookupOp::Sqrt { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::sqrt(&x, scale.into()))
-                }
-                LookupOp::Rsqrt { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::rsqrt(&x, scale.into()))
-                }
                 LookupOp::Erf { scale } => {
                     Ok::<_, TensorError>(tensor::ops::nonlinearities::erffunc(&x, scale.into()))
                 }
-                LookupOp::Exp { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::exp(&x, scale.into()))
-                }
-                LookupOp::Ln { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ln(&x, scale.into()))
-                }
+                LookupOp::Exp { scale, base } => Ok::<_, TensorError>(
+                    tensor::ops::nonlinearities::exp(&x, scale.into(), base.into()),
+                ),
                 LookupOp::Cos { scale } => {
                     Ok::<_, TensorError>(tensor::ops::nonlinearities::cos(&x, scale.into()))
                 }
@@ -171,16 +158,14 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Lookup
     /// Returns the name of the operation
     fn as_string(&self) -> String {
         match self {
-            LookupOp::RoundHalfToEven { scale } => format!("ROUND_HALF_TO_EVEN(scale={})", scale),
+            LookupOp::Ln { scale } => format!("LN(scale={})", scale),
+            LookupOp::PowersOfTwo { scale } => format!("POWERS_OF_TWO(scale={})", scale),
+            LookupOp::IsOdd => "IS_ODD".to_string(),
             LookupOp::Pow { a, scale } => format!("POW(scale={}, exponent={})", scale, a),
             LookupOp::Div { denom, .. } => format!("DIV(denom={})", denom),
-            LookupOp::Cast { scale } => format!("CAST(scale={})", scale),
-            LookupOp::Ln { scale } => format!("LN(scale={})", scale),
             LookupOp::Sigmoid { scale } => format!("SIGMOID(scale={})", scale),
-            LookupOp::Sqrt { scale } => format!("SQRT(scale={})", scale),
             LookupOp::Erf { scale } => format!("ERF(scale={})", scale),
-            LookupOp::Rsqrt { scale } => format!("RSQRT(scale={})", scale),
-            LookupOp::Exp { scale } => format!("EXP(scale={})", scale),
+            LookupOp::Exp { scale, base } => format!("EXP(scale={}, base={})", scale, base),
             LookupOp::Tan { scale } => format!("TAN(scale={})", scale),
             LookupOp::ATan { scale } => format!("ATAN(scale={})", scale),
             LookupOp::Tanh { scale } => format!("TANH(scale={})", scale),
@@ -213,13 +198,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Lookup
 
     /// Returns the scale of the output of the operation.
     fn out_scale(&self, inputs_scale: Vec<crate::Scale>) -> Result<crate::Scale, CircuitError> {
-        let scale = match self {
-            LookupOp::Cast { scale } => {
-                let in_scale = inputs_scale[0];
-                in_scale + multiplier_to_scale(1. / scale.0 as f64)
-            }
-            _ => inputs_scale[0],
-        };
+        let scale = inputs_scale[0];
         Ok(scale)
     }
 

src/circuit/ops/region.rs

@@ -474,6 +474,17 @@ impl<'a, F: PrimeField + TensorType + PartialOrd + std::hash::Hash> RegionCtx<'a
         Ok(())
     }
 
+    /// Update the max and min forcefully 
+    pub fn update_max_min_lookup_inputs_force(
+        &mut self,
+        min: IntegerRep,
+        max: IntegerRep,
+    ) -> Result<(), CircuitError> {
+        self.statistics.max_lookup_inputs = self.statistics.max_lookup_inputs.max(max);
+        self.statistics.min_lookup_inputs = self.statistics.min_lookup_inputs.min(min);
+        Ok(())
+    }
+
     /// Update the max and min from inputs
     pub fn update_max_min_lookup_range(&mut self, range: Range) -> Result<(), CircuitError> {
         if range.0 > range.1 {

src/circuit/table.rs

@@ -150,12 +150,16 @@ pub fn num_cols_required(range_len: IntegerRep, col_size: usize) -> usize {
 }
 
 impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
+    /// get largest element represented by the range
+    pub fn largest(&self) -> IntegerRep {
+        self.range.0 + (self.col_size * self.table_inputs.len() - 1) as IntegerRep
+    }
     fn name(&self) -> String {
         format!(
             "{}_{}_{}",
             self.nonlinearity.as_path(),
             self.range.0,
-            self.range.1
+            self.largest()
         )
     }
     /// Configures the table.
@@ -222,7 +226,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
         }
 
         let smallest = self.range.0;
-        let largest = self.range.1;
+        let largest = self.largest();
 
         let gen_table = || -> Result<(Tensor<F>, Tensor<F>), crate::tensor::TensorError> {
             let inputs = Tensor::from(smallest..=largest)
@@ -291,6 +295,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Table<F> {
 
                                 row_offset += chunk_idx * self.col_size;
                                 let (x, y) = self.cartesian_coord(row_offset);
+
                                 if !preassigned_input {
                                     table.assign_cell(
                                         || format!("nl_i_col row {}", row_offset),

src/commands.rs

@@ -333,18 +333,6 @@ impl<'source> FromPyObject<'source> for ContractType {
         }
     }
 }
-// not wasm
-use lazy_static::lazy_static;
-
-// if CARGO VERSION is 0.0.0 replace with "source - no compatibility guaranteed"
-lazy_static! {
-    /// The version of the ezkl library
-    pub static ref VERSION: &'static str =  if env!("CARGO_PKG_VERSION") == "0.0.0" {
-       "source - no compatibility guaranteed"
-    } else {
-        env!("CARGO_PKG_VERSION")
-    };
-}
 
 /// Get the styles for the CLI
 pub fn get_styles() -> clap::builder::Styles {
@@ -395,7 +383,7 @@ pub fn print_completions<G: Generator>(gen: G, cmd: &mut Command) {
 #[allow(missing_docs)]
 #[derive(Parser, Debug, Clone)]
 #[command(author, about, long_about = None)]
-#[clap(version = *VERSION, styles = get_styles(), trailing_var_arg = true)]
+#[clap(version = crate::version(), styles = get_styles(), trailing_var_arg = true)]
 pub struct Cli {
     /// If provided, outputs the completion file for given shell
     #[clap(long = "generate", value_parser)]
@@ -486,9 +474,6 @@ pub enum Commands {
         /// max logrows to use for calibration, 26 is the max public SRS size
         #[arg(long, value_hint = clap::ValueHint::Other)]
         max_logrows: Option<u32>,
-        // whether to only range check rebases (instead of trying both range check and lookup)
-        #[arg(long, default_value = DEFAULT_ONLY_RANGE_CHECK_REBASE, action = clap::ArgAction::SetTrue)]
-        only_range_check_rebase: Option<bool>,
     },
 
     /// Generates a dummy SRS

src/execute.rs

@@ -1,10 +1,13 @@
 use crate::circuit::region::RegionSettings;
 use crate::circuit::CheckMode;
 use crate::commands::CalibrationTarget;
-use crate::eth::{deploy_contract_via_solidity, deploy_da_verifier_via_solidity};
+use crate::eth::{
+    deploy_contract_via_solidity, deploy_da_verifier_via_solidity, fix_da_multi_sol,
+    fix_da_single_sol,
+};
 #[allow(unused_imports)]
-use crate::eth::{fix_da_sol, get_contract_artifacts, verify_proof_via_solidity};
-use crate::graph::input::GraphData;
+use crate::eth::{get_contract_artifacts, verify_proof_via_solidity};
+use crate::graph::input::{Calls, GraphData};
 use crate::graph::{GraphCircuit, GraphSettings, GraphWitness, Model};
 use crate::graph::{TestDataSource, TestSources};
 use crate::pfsys::evm::aggregation_kzg::{AggregationCircuit, PoseidonTranscript};
@@ -140,7 +143,6 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
             scales,
             scale_rebase_multiplier,
             max_logrows,
-            only_range_check_rebase,
         } => calibrate(
             model.unwrap_or(DEFAULT_MODEL.into()),
             data.unwrap_or(DEFAULT_DATA.into()),
@@ -149,7 +151,6 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
             lookup_safety_margin,
             scales,
             scale_rebase_multiplier,
-            only_range_check_rebase.unwrap_or(DEFAULT_ONLY_RANGE_CHECK_REBASE.parse().unwrap()),
             max_logrows,
         )
         .await
@@ -671,10 +672,10 @@ pub(crate) async fn get_srs_cmd(
             let srs_uri = format!("{}{}", PUBLIC_SRS_URL, k);
             let mut reader = Cursor::new(fetch_srs(&srs_uri).await?);
             // check the SRS
-                let pb = init_spinner();
-                pb.set_message("Validating SRS (this may take a while) ...");
+            let pb = init_spinner();
+            pb.set_message("Validating SRS (this may take a while) ...");
             let params = ParamsKZG::<Bn256>::read(&mut reader)?;
-                pb.finish_with_message("SRS validated.");
+            pb.finish_with_message("SRS validated.");
 
             info!("Saving SRS to disk...");
             let computed_srs_path = get_srs_path(k, srs_path.clone(), commitment);
@@ -682,7 +683,10 @@ pub(crate) async fn get_srs_cmd(
             let mut buffer = BufWriter::with_capacity(*EZKL_BUF_CAPACITY, &mut file);
             params.write(&mut buffer)?;
 
-            info!("Saved SRS to {}.", computed_srs_path.as_os_str().to_str().unwrap_or("disk"));
+            info!(
+                "Saved SRS to {}.",
+                computed_srs_path.as_os_str().to_str().unwrap_or("disk")
+            );
 
             info!("SRS downloaded");
         } else {
@@ -728,7 +732,7 @@ pub(crate) async fn gen_witness(
         None
     };
 
-        let mut input = circuit.load_graph_input(&data).await?;
+    let mut input = circuit.load_graph_input(&data).await?;
     #[cfg(any(not(feature = "ezkl"), target_arch = "wasm32"))]
     let mut input = circuit.load_graph_input(&data)?;
 
@@ -968,7 +972,6 @@ pub(crate) async fn calibrate(
     lookup_safety_margin: f64,
     scales: Option<Vec<crate::Scale>>,
     scale_rebase_multiplier: Vec<u32>,
-    only_range_check_rebase: bool,
     max_logrows: Option<u32>,
 ) -> Result<GraphSettings, EZKLError> {
     use log::error;
@@ -1004,12 +1007,6 @@ pub(crate) async fn calibrate(
         (11..14).collect::<Vec<crate::Scale>>()
     };
 
-    let div_rebasing = if only_range_check_rebase {
-        vec![false]
-    } else {
-        vec![true, false]
-    };
-
     let mut found_params: Vec<GraphSettings> = vec![];
 
     // 2 x 2 grid
@@ -1047,12 +1044,6 @@ pub(crate) async fn calibrate(
         .map(|(a, b)| (*a, *b))
         .collect::<Vec<((crate::Scale, crate::Scale), u32)>>();
 
-    let range_grid = range_grid
-        .iter()
-        .cartesian_product(div_rebasing.iter())
-        .map(|(a, b)| (*a, *b))
-        .collect::<Vec<(((crate::Scale, crate::Scale), u32), bool)>>();
-
     let mut forward_pass_res = HashMap::new();
 
     let pb = init_bar(range_grid.len() as u64);
@@ -1061,30 +1052,23 @@ pub(crate) async fn calibrate(
     let mut num_failed = 0;
     let mut num_passed = 0;
 
-    for (((input_scale, param_scale), scale_rebase_multiplier), div_rebasing) in range_grid {
+    for ((input_scale, param_scale), scale_rebase_multiplier) in range_grid {
         pb.set_message(format!(
-            "i-scale: {}, p-scale: {}, rebase-(x): {}, div-rebase: {}, fail: {}, pass: {}",
+            "i-scale: {}, p-scale: {}, rebase-(x): {}, fail: {}, pass: {}",
             input_scale.to_string().blue(),
             param_scale.to_string().blue(),
-            scale_rebase_multiplier.to_string().blue(),
-            div_rebasing.to_string().yellow(),
+            scale_rebase_multiplier.to_string().yellow(),
             num_failed.to_string().red(),
             num_passed.to_string().green()
         ));
 
-        let key = (
-            input_scale,
-            param_scale,
-            scale_rebase_multiplier,
-            div_rebasing,
-        );
+        let key = (input_scale, param_scale, scale_rebase_multiplier);
         forward_pass_res.insert(key, vec![]);
 
         let local_run_args = RunArgs {
             input_scale,
             param_scale,
             scale_rebase_multiplier,
-            div_rebasing,
             lookup_range: (IntegerRep::MIN, IntegerRep::MAX),
             ..settings.run_args.clone()
         };
@@ -1188,7 +1172,6 @@ pub(crate) async fn calibrate(
             let found_run_args = RunArgs {
                 input_scale: new_settings.run_args.input_scale,
                 param_scale: new_settings.run_args.param_scale,
-                div_rebasing: new_settings.run_args.div_rebasing,
                 lookup_range: new_settings.run_args.lookup_range,
                 logrows: new_settings.run_args.logrows,
                 scale_rebase_multiplier: new_settings.run_args.scale_rebase_multiplier,
@@ -1296,7 +1279,6 @@ pub(crate) async fn calibrate(
             best_params.run_args.input_scale,
             best_params.run_args.param_scale,
             best_params.run_args.scale_rebase_multiplier,
-            best_params.run_args.div_rebasing,
         ))
         .ok_or("no params found")?
         .iter()
@@ -1477,13 +1459,24 @@ pub(crate) async fn create_evm_data_attestation(
     // if input is not provided, we just instantiate dummy input data
     let data = GraphData::from_path(input).unwrap_or(GraphData::new(DataSource::File(vec![])));
 
+    // The number of input and output instances we attest to for the single call data attestation
+    let mut input_len = None;
+    let mut output_len = None;
+
     let output_data = if let Some(DataSource::OnChain(source)) = data.output_data {
         if visibility.output.is_private() {
             return Err("private output data on chain is not supported on chain".into());
         }
         let mut on_chain_output_data = vec![];
-        for call in source.calls {
-            on_chain_output_data.push(call);
+        match source.calls {
+            Calls::Multiple(calls) => {
+                for call in calls {
+                    on_chain_output_data.push(call);
+                }
+            }
+            Calls::Single(call) => {
+                output_len = Some(call.len);
+            }
         }
         Some(on_chain_output_data)
     } else {
@@ -1495,8 +1488,15 @@ pub(crate) async fn create_evm_data_attestation(
             return Err("private input data on chain is not supported on chain".into());
         }
         let mut on_chain_input_data = vec![];
-        for call in source.calls {
-            on_chain_input_data.push(call);
+        match source.calls {
+            Calls::Multiple(calls) => {
+                for call in calls {
+                    on_chain_input_data.push(call);
+                }
+            }
+            Calls::Single(call) => {
+                input_len = Some(call.len);
+            }
         }
         Some(on_chain_input_data)
     } else {
@@ -1523,13 +1523,24 @@ pub(crate) async fn create_evm_data_attestation(
         None
     };
 
-    let output = fix_da_sol(input_data, output_data, commitment_bytes)?;
-    let mut f = File::create(sol_code_path.clone())?;
-    let _ = f.write(output.as_bytes());
-    // fetch abi of the contract
-    let (abi, _, _) = get_contract_artifacts(sol_code_path, "DataAttestation", 0).await?;
-    // save abi to file
-    serde_json::to_writer(std::fs::File::create(abi_path)?, &abi)?;
+    // if either input_len or output_len is Some then we are in the single call data attestation mode
+    if input_len.is_some() || output_len.is_some() {
+        let output = fix_da_single_sol(input_len, output_len)?;
+        let mut f = File::create(sol_code_path.clone())?;
+        let _ = f.write(output.as_bytes());
+        // fetch abi of the contract
+        let (abi, _, _) = get_contract_artifacts(sol_code_path, "DataAttestationSingle", 0).await?;
+        // save abi to file
+        serde_json::to_writer(std::fs::File::create(abi_path)?, &abi)?;
+    } else {
+        let output = fix_da_multi_sol(input_data, output_data, commitment_bytes)?;
+        let mut f = File::create(sol_code_path.clone())?;
+        let _ = f.write(output.as_bytes());
+        // fetch abi of the contract
+        let (abi, _, _) = get_contract_artifacts(sol_code_path, "DataAttestationMulti", 0).await?;
+        // save abi to file
+        serde_json::to_writer(std::fs::File::create(abi_path)?, &abi)?;
+    }
 
     Ok(String::new())
 }
@@ -2022,7 +2033,7 @@ pub(crate) fn mock_aggregate(
         }
     }
     // proof aggregation
-        let pb = {
+    let pb = {
         let pb = init_spinner();
         pb.set_message("Aggregating (may take a while)...");
         pb
@@ -2033,7 +2044,7 @@ pub(crate) fn mock_aggregate(
     let prover = halo2_proofs::dev::MockProver::run(logrows, &circuit, vec![circuit.instances()])
         .map_err(|e| ExecutionError::MockProverError(e.to_string()))?;
     prover.verify().map_err(ExecutionError::VerifyError)?;
-        pb.finish_with_message("Done.");
+    pb.finish_with_message("Done.");
     Ok(String::new())
 }
 
@@ -2127,7 +2138,7 @@ pub(crate) fn aggregate(
     }
 
     // proof aggregation
-        let pb = {
+    let pb = {
         let pb = init_spinner();
         pb.set_message("Aggregating (may take a while)...");
         pb
@@ -2276,7 +2287,7 @@ pub(crate) fn aggregate(
     );
     snark.save(&proof_path)?;
 
-        pb.finish_with_message("Done.");
+    pb.finish_with_message("Done.");
 
     Ok(snark)
 }

src/graph/input.rs

@@ -128,7 +128,9 @@ impl FileSourceInner {
     /// Convert to a field element
     pub fn to_field(&self, scale: crate::Scale) -> Fp {
         match self {
-            FileSourceInner::Float(f) => integer_rep_to_felt(quantize_float(f, 0.0, scale).unwrap()),
+            FileSourceInner::Float(f) => {
+                integer_rep_to_felt(quantize_float(f, 0.0, scale).unwrap())
+            }
             FileSourceInner::Bool(f) => {
                 if *f {
                     Fp::one()
@@ -155,19 +157,80 @@ impl FileSourceInner {
     }
 }
 
+/// Call type for attested inputs on-chain
+#[derive(Clone, Debug, PartialOrd, PartialEq)]
+pub enum Calls {
+    /// Vector of calls to accounts, each returning an attested data point
+    Multiple(Vec<CallsToAccount>),
+    /// Single call to account, returning an array of attested data points
+    Single(CallToAccount),
+}
+
+impl Default for Calls {
+    fn default() -> Self {
+        Calls::Multiple(Vec::new())
+    }
+}
 /// Inner elements of inputs/outputs coming from on-chain
 #[derive(Clone, Debug, Deserialize, Serialize, Default, PartialOrd, PartialEq)]
 pub struct OnChainSource {
-    /// Vector of calls to accounts
-    pub calls: Vec<CallsToAccount>,
+    /// Calls to accounts
+    pub calls: Calls,
     /// RPC url
     pub rpc: RPCUrl,
 }
 
 impl OnChainSource {
-    /// Create a new OnChainSource
-    pub fn new(calls: Vec<CallsToAccount>, rpc: RPCUrl) -> Self {
-        OnChainSource { calls, rpc }
+    /// Create a new OnChainSource with multiple calls
+    pub fn new_multiple(calls: Vec<CallsToAccount>, rpc: RPCUrl) -> Self {
+        OnChainSource {
+            calls: Calls::Multiple(calls),
+            rpc,
+        }
+    }
+
+    /// Create a new OnChainSource with a single call
+    pub fn new_single(call: CallToAccount, rpc: RPCUrl) -> Self {
+        OnChainSource {
+            calls: Calls::Single(call),
+            rpc,
+        }
+    }
+}
+
+impl Serialize for Calls {
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: Serializer,
+    {
+        match self {
+            Calls::Single(data) => data.serialize(serializer),
+            Calls::Multiple(data) => data.serialize(serializer),
+        }
+    }
+}
+
+// !!! ALWAYS USE JSON SERIALIZATION FOR GRAPH INPUT
+// UNTAGGED ENUMS WONT WORK :( as highlighted here:
+impl<'de> Deserialize<'de> for Calls {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+        let this_json: Box<serde_json::value::RawValue> = Deserialize::deserialize(deserializer)?;
+
+        let multiple_try: Result<Vec<CallsToAccount>, _> = serde_json::from_str(this_json.get());
+        if let Ok(t) = multiple_try {
+            return Ok(Calls::Multiple(t));
+        }
+        let single_try: Result<CallToAccount, _> = serde_json::from_str(this_json.get());
+        if let Ok(t) = single_try {
+            return Ok(Calls::Single(t));
+        }
+
+        Err(serde::de::Error::custom(
+            "failed to deserialize FileSourceInner",
+        ))
     }
 }
 
@@ -277,7 +340,8 @@ impl OnChainSource {
         rpc: Option<&str>,
     ) -> Result<(Vec<Tensor<Fp>>, Self), GraphError> {
         use crate::eth::{
-            evm_quantize, read_on_chain_inputs, test_on_chain_data, DEFAULT_ANVIL_ENDPOINT,
+            evm_quantize_multi, read_on_chain_inputs_multi, test_on_chain_data,
+            DEFAULT_ANVIL_ENDPOINT,
         };
         use log::debug;
 
@@ -296,7 +360,7 @@ impl OnChainSource {
         let calls_to_accounts = test_on_chain_data(client.clone(), data).await?;
         debug!("Calls to accounts: {:?}", calls_to_accounts);
         let inputs =
-            read_on_chain_inputs(client.clone(), client_address, &calls_to_accounts).await?;
+            read_on_chain_inputs_multi(client.clone(), client_address, &calls_to_accounts).await?;
         debug!("Inputs: {:?}", inputs);
 
         let mut quantized_evm_inputs = vec![];
@@ -304,7 +368,7 @@ impl OnChainSource {
         let mut prev = 0;
         for (idx, i) in data.iter().enumerate() {
             quantized_evm_inputs.extend(
-                evm_quantize(
+                evm_quantize_multi(
                     client.clone(),
                     vec![scales[idx]; i.len()],
                     &(
@@ -330,7 +394,7 @@ impl OnChainSource {
         // Fill the input_data field of the GraphData struct
         Ok((
             inputs,
-            OnChainSource::new(calls_to_accounts.clone(), used_rpc),
+            OnChainSource::new_multiple(calls_to_accounts.clone(), used_rpc),
         ))
     }
 }
@@ -350,6 +414,24 @@ pub struct CallsToAccount {
     /// Address of the contract to read the data from.
     pub address: String,
 }
+
+/// Defines a view only call to accounts to fetch the on-chain input data.
+/// This data will be included as part of the first elements in the publicInputs
+/// for the sol evm verifier and will be  verifyWithDataAttestation.sol
+#[derive(Clone, Debug, Deserialize, Serialize, Default, PartialOrd, PartialEq)]
+pub struct CallToAccount {
+    /// The call_data is a byte strings representing the ABI encoded function call to
+    /// read the data from the address. This call must return a single array of integers that can be
+    /// be safely cast to the int128 type in solidity.
+    pub call_data: Call,
+    /// The number of decimals for f32 conversion of all of the elements returned from the
+    /// call.
+    pub decimals: Decimals,
+    /// Address of the contract to read the data from.
+    pub address: String,
+    /// The number of elements returned from the call.
+    pub len: usize,
+}
 /// Enum that defines source of the inputs/outputs to the EZKL model
 #[derive(Clone, Debug, Serialize, PartialOrd, PartialEq)]
 #[serde(untagged)]
@@ -600,6 +682,28 @@ impl ToPyObject for CallsToAccount {
     }
 }
 
+#[cfg(feature = "python-bindings")]
+impl ToPyObject for CallToAccount {
+    fn to_object(&self, py: Python) -> PyObject {
+        let dict = PyDict::new(py);
+        dict.set_item("account", &self.address).unwrap();
+        dict.set_item("call_data", &self.call_data).unwrap();
+        dict.set_item("decimals", &self.decimals).unwrap();
+        dict.set_item("len", &self.len).unwrap();
+        dict.to_object(py)
+    }
+}
+
+#[cfg(feature = "python-bindings")]
+impl ToPyObject for Calls {
+    fn to_object(&self, py: Python) -> PyObject {
+        match self {
+            Calls::Multiple(calls) => calls.to_object(py),
+            Calls::Single(call) => call.to_object(py),
+        }
+    }
+}
+
 #[cfg(feature = "python-bindings")]
 impl ToPyObject for DataSource {
     fn to_object(&self, py: Python) -> PyObject {

src/graph/mod.rs

@@ -133,6 +133,8 @@ pub struct GraphWitness {
     pub min_lookup_inputs: IntegerRep,
     /// max range check size
     pub max_range_size: IntegerRep,
+    /// (optional) version of ezkl used
+    pub version: Option<String>,
 }
 
 impl GraphWitness {
@@ -161,6 +163,7 @@ impl GraphWitness {
             max_lookup_inputs: 0,
             min_lookup_inputs: 0,
             max_range_size: 0,
+            version: None,
         }
     }
 
@@ -1001,11 +1004,24 @@ impl GraphCircuit {
         shapes: &Vec<Vec<usize>>,
         scales: Vec<crate::Scale>,
     ) -> Result<Vec<Tensor<Fp>>, GraphError> {
-        use crate::eth::{evm_quantize, read_on_chain_inputs, setup_eth_backend};
+        use crate::eth::{
+            evm_quantize_multi, evm_quantize_single, read_on_chain_inputs_multi,
+            read_on_chain_inputs_single, setup_eth_backend,
+        };
         let (client, client_address) = setup_eth_backend(Some(&source.rpc), None).await?;
-        let inputs = read_on_chain_inputs(client.clone(), client_address, &source.calls).await?;
-        // quantize the supplied data using the provided scale + QuantizeData.sol
-        let quantized_evm_inputs = evm_quantize(client, scales, &inputs).await?;
+        let quantized_evm_inputs = match source.calls {
+            input::Calls::Single(call) => {
+                let (inputs, decimals) =
+                    read_on_chain_inputs_single(client.clone(), client_address, call).await?;
+
+                evm_quantize_single(client, scales, &inputs, decimals).await?
+            }
+            input::Calls::Multiple(calls) => {
+                let inputs =
+                    read_on_chain_inputs_multi(client.clone(), client_address, &calls).await?;
+                evm_quantize_multi(client, scales, &inputs).await?
+            }
+        };
         // on-chain data has already been quantized at this point. Just need to reshape it and push into tensor vector
         let mut inputs: Vec<Tensor<Fp>> = vec![];
         for (input, shape) in [quantized_evm_inputs].iter().zip(shapes) {
@@ -1350,6 +1366,7 @@ impl GraphCircuit {
             max_lookup_inputs: model_results.max_lookup_inputs,
             min_lookup_inputs: model_results.min_lookup_inputs,
             max_range_size: model_results.max_range_size,
+            version: Some(crate::version().to_string()),
         };
 
         witness.generate_rescaled_elements(

src/graph/model.rs

@@ -915,20 +915,9 @@ impl Model {
                         if scales.contains_key(&i) {
                             let scale_diff = n.out_scale - scales[&i];
                             n.opkind = if scale_diff > 0 {
-                                RebaseScale::rebase(
-                                    n.opkind,
-                                    scales[&i],
-                                    n.out_scale,
-                                    1,
-                                    run_args.div_rebasing,
-                                )
+                                RebaseScale::rebase(n.opkind, scales[&i], n.out_scale, 1)
                             } else {
-                                RebaseScale::rebase_up(
-                                    n.opkind,
-                                    scales[&i],
-                                    n.out_scale,
-                                    run_args.div_rebasing,
-                                )
+                                RebaseScale::rebase_up(n.opkind, scales[&i], n.out_scale)
                             };
                             n.out_scale = scales[&i];
                         }

src/graph/node.rs

@@ -120,7 +120,6 @@ impl RebaseScale {
         global_scale: crate::Scale,
         op_out_scale: crate::Scale,
         scale_rebase_multiplier: u32,
-        div_rebasing: bool,
     ) -> SupportedOp {
         if (op_out_scale > (global_scale * scale_rebase_multiplier as i32))
             && !inner.is_constant()
@@ -137,7 +136,6 @@ impl RebaseScale {
                     multiplier,
                     rebase_op: HybridOp::Div {
                         denom: crate::circuit::utils::F32((multiplier) as f32),
-                        use_range_check_for_int: !div_rebasing,
                     },
                     original_scale: op.original_scale,
                 })
@@ -148,7 +146,6 @@ impl RebaseScale {
                     multiplier,
                     rebase_op: HybridOp::Div {
                         denom: crate::circuit::utils::F32(multiplier as f32),
-                        use_range_check_for_int: !div_rebasing,
                     },
                     original_scale: op_out_scale,
                 })
@@ -163,7 +160,6 @@ impl RebaseScale {
         inner: SupportedOp,
         target_scale: crate::Scale,
         op_out_scale: crate::Scale,
-        div_rebasing: bool,
     ) -> SupportedOp {
         if (op_out_scale < (target_scale)) && !inner.is_constant() && !inner.is_input() {
             let multiplier = scale_to_multiplier(op_out_scale - target_scale);
@@ -176,7 +172,6 @@ impl RebaseScale {
                     original_scale: op.original_scale,
                     rebase_op: HybridOp::Div {
                         denom: crate::circuit::utils::F32((multiplier) as f32),
-                        use_range_check_for_int: !div_rebasing,
                     },
                 })
             } else {
@@ -187,7 +182,6 @@ impl RebaseScale {
                     original_scale: op_out_scale,
                     rebase_op: HybridOp::Div {
                         denom: crate::circuit::utils::F32(multiplier as f32),
-                        use_range_check_for_int: !div_rebasing,
                     },
                 })
             }
@@ -595,13 +589,7 @@ impl Node {
         let mut out_scale = opkind.out_scale(in_scales.clone())?;
         // rescale the inputs if necessary to get consistent fixed points, we select the largest scale (highest precision)
         let global_scale = scales.get_max();
-        opkind = RebaseScale::rebase(
-            opkind,
-            global_scale,
-            out_scale,
-            scales.rebase_multiplier,
-            run_args.div_rebasing,
-        );
+        opkind = RebaseScale::rebase(opkind, global_scale, out_scale, scales.rebase_multiplier);
 
         out_scale = opkind.out_scale(in_scales)?;
 

src/graph/utilities.rs

@@ -279,6 +279,8 @@ pub fn new_op_from_onnx(
     symbol_values: &SymbolValues,
     run_args: &crate::RunArgs,
 ) -> Result<(SupportedOp, Vec<usize>), GraphError> {
+    use std::f64::consts::E;
+
     use tract_onnx::tract_core::ops::array::Trilu;
 
     use crate::circuit::InputType;
@@ -764,7 +766,7 @@ pub fn new_op_from_onnx(
                 .collect::<Vec<_>>();
 
             if inputs.len() == 2 {
-                if const_inputs.len() > 0 {
+                if !const_inputs.is_empty() {
                     let const_idx = const_inputs[0];
                     let boxed_op = inputs[const_idx].opkind();
                     let unit = if let Some(c) = extract_const_raw_values(boxed_op) {
@@ -803,7 +805,7 @@ pub fn new_op_from_onnx(
             }
         }
         "Recip" => {
-            let in_scale = inputs[0].out_scales()[0];
+            let in_scale = input_scales[0];
             let max_scale = std::cmp::max(scales.get_max(), in_scale);
             // If the input scale is larger than the params scale
             SupportedOp::Hybrid(HybridOp::Recip {
@@ -837,61 +839,76 @@ pub fn new_op_from_onnx(
         "Abs" => SupportedOp::Linear(PolyOp::Abs),
         "Neg" => SupportedOp::Linear(PolyOp::Neg),
         "HardSwish" => SupportedOp::Nonlinear(LookupOp::HardSwish {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Sigmoid" => SupportedOp::Nonlinear(LookupOp::Sigmoid {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
-        "Sqrt" => SupportedOp::Nonlinear(LookupOp::Sqrt {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
-        }),
-        "Rsqrt" => SupportedOp::Nonlinear(LookupOp::Rsqrt {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+        "Sqrt" => SupportedOp::Hybrid(HybridOp::Sqrt {
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
+        "Rsqrt" => {
+            let in_scale = input_scales[0];
+            let max_scale = std::cmp::max(scales.get_max(), in_scale);
+            SupportedOp::Hybrid(HybridOp::Rsqrt {
+                input_scale: (scale_to_multiplier(in_scale) as f32).into(),
+                output_scale: (scale_to_multiplier(max_scale) as f32).into(),
+            })
+        }
         "Exp" => SupportedOp::Nonlinear(LookupOp::Exp {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
-        }),
-        "Ln" => SupportedOp::Nonlinear(LookupOp::Ln {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
+            base: E.into(),
         }),
+        "Ln" => {
+            if run_args.bounded_log_lookup {
+                SupportedOp::Hybrid(HybridOp::Ln {
+                    scale: scale_to_multiplier(input_scales[0]).into(),
+                })
+            } else {
+                SupportedOp::Nonlinear(LookupOp::Ln {
+                    scale: scale_to_multiplier(input_scales[0]).into(),
+                })
+            }
+        }
+
         "Sin" => SupportedOp::Nonlinear(LookupOp::Sin {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Cos" => SupportedOp::Nonlinear(LookupOp::Cos {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Tan" => SupportedOp::Nonlinear(LookupOp::Tan {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Asin" => SupportedOp::Nonlinear(LookupOp::ASin {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Acos" => SupportedOp::Nonlinear(LookupOp::ACos {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Atan" => SupportedOp::Nonlinear(LookupOp::ATan {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Sinh" => SupportedOp::Nonlinear(LookupOp::Sinh {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Cosh" => SupportedOp::Nonlinear(LookupOp::Cosh {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Tanh" => SupportedOp::Nonlinear(LookupOp::Tanh {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Asinh" => SupportedOp::Nonlinear(LookupOp::ASinh {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Acosh" => SupportedOp::Nonlinear(LookupOp::ACosh {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Atanh" => SupportedOp::Nonlinear(LookupOp::ATanh {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Erf" => SupportedOp::Nonlinear(LookupOp::Erf {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
         }),
         "Source" => {
             let dt = node.outputs[0].fact.datum_type;
@@ -935,11 +952,9 @@ pub fn new_op_from_onnx(
                         replace_const(
                             0,
                             0,
-                            SupportedOp::Nonlinear(LookupOp::Cast {
-                                scale: crate::circuit::utils::F32(scale_to_multiplier(
-                                    input_scales[0],
-                                )
-                                    as f32),
+                            SupportedOp::Hybrid(HybridOp::Floor {
+                                scale: scale_to_multiplier(input_scales[0]).into(),
+                                legs: run_args.decomp_legs,
                             }),
                         )?
                     } else {
@@ -1045,7 +1060,7 @@ pub fn new_op_from_onnx(
                 }
             };
 
-            let in_scale = inputs[0].out_scales()[0];
+            let in_scale = input_scales[0];
             let max_scale = std::cmp::max(scales.get_max(), in_scale);
 
             SupportedOp::Hybrid(HybridOp::Softmax {
@@ -1084,19 +1099,20 @@ pub fn new_op_from_onnx(
             })
         }
         "Ceil" => SupportedOp::Hybrid(HybridOp::Ceil {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
             legs: run_args.decomp_legs,
         }),
         "Floor" => SupportedOp::Hybrid(HybridOp::Floor {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
             legs: run_args.decomp_legs,
         }),
         "Round" => SupportedOp::Hybrid(HybridOp::Round {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+            scale: scale_to_multiplier(input_scales[0]).into(),
             legs: run_args.decomp_legs,
         }),
-        "RoundHalfToEven" => SupportedOp::Nonlinear(LookupOp::RoundHalfToEven {
-            scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+        "RoundHalfToEven" => SupportedOp::Hybrid(HybridOp::RoundHalfToEven {
+            scale: scale_to_multiplier(input_scales[0]).into(),
+            legs: run_args.decomp_legs,
         }),
         "Sign" => SupportedOp::Linear(PolyOp::Sign),
         "Pow" => {
@@ -1116,12 +1132,62 @@ pub fn new_op_from_onnx(
                     SupportedOp::Linear(PolyOp::Pow(exponent as u32))
                 } else {
                     SupportedOp::Nonlinear(LookupOp::Pow {
-                        scale: scale_to_multiplier(inputs[0].out_scales()[0]).into(),
+                        scale: scale_to_multiplier(input_scales[0]).into(),
                         a: crate::circuit::utils::F32(exponent),
                     })
                 }
             } else {
-                unimplemented!("only support constant pow for now")
+                if let Some(c) = inputs[0].opkind().get_mutable_constant() {
+                    inputs[0].decrement_use();
+                    deleted_indices.push(0);
+                    if c.raw_values.len() > 1 {
+                        unimplemented!("only support scalar base")
+                    }
+
+                    let base = c.raw_values[0];
+
+                    SupportedOp::Nonlinear(LookupOp::Exp {
+                        scale: scale_to_multiplier(input_scales[1]).into(),
+                        base: base.into(),
+                    })
+                } else {
+                    unimplemented!("only support constant base or pow for now")
+                }
+            }
+        }
+        "Div" => {
+            let const_idx = inputs
+                .iter()
+                .enumerate()
+                .filter(|(_, n)| n.is_constant())
+                .map(|(i, _)| i)
+                .collect::<Vec<_>>();
+
+            if const_idx.len() > 1 {
+                return Err(GraphError::InvalidDims(idx, "div".to_string()));
+            }
+
+            let const_idx = const_idx[0];
+
+            if const_idx != 1 {
+                unimplemented!("only support div with constant as second input")
+            }
+
+            if let Some(c) = inputs[const_idx].opkind().get_mutable_constant() {
+                if c.raw_values.len() == 1 && c.raw_values[0] != 0. {
+                    inputs[const_idx].decrement_use();
+                    deleted_indices.push(const_idx);
+                    // get the non constant index
+                    let denom = c.raw_values[0];
+
+                    SupportedOp::Hybrid(HybridOp::Div {
+                        denom: denom.into(),
+                    })
+                } else {
+                    unimplemented!("only support non zero divisors of size 1")
+                }
+            } else {
+                unimplemented!("only support div with constant as second input")
             }
         }
         "Cube" => SupportedOp::Linear(PolyOp::Pow(3)),

src/lib.rs

@@ -108,6 +108,18 @@ use serde::{Deserialize, Serialize};
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use tosubcommand::ToFlags;
 
+// if CARGO VERSION is 0.0.0 replace with "source - no compatibility guaranteed"
+/// The version of the ezkl library
+const VERSION: &str = env!("CARGO_PKG_VERSION");
+
+/// Get the version of the library
+pub fn version() -> &'static str {
+    match VERSION {
+        "0.0.0" => "source - no compatibility guaranteed",
+        _ => VERSION,
+    }
+}
+
 /// Bindings managment
 #[cfg(any(
     feature = "ios-bindings",
@@ -297,8 +309,6 @@ pub struct RunArgs {
         all(feature = "ezkl", not(target_arch = "wasm32")),
         arg(long, default_value = "false")
     )]
-    /// Rebase the scale using lookup table for division instead of using a range check
-    pub div_rebasing: bool,
     /// Should constants with 0.0 fraction be rebased to scale 0
     #[cfg_attr(
         all(feature = "ezkl", not(target_arch = "wasm32")),
@@ -317,11 +327,18 @@ pub struct RunArgs {
     #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(long, default_value = "2", value_hint = clap::ValueHint::Other))]
     /// the number of legs used for decompositions
     pub decomp_legs: usize,
+    #[cfg_attr(
+        all(feature = "ezkl", not(target_arch = "wasm32")),
+        arg(long, default_value = "false")
+    )]
+    /// use unbounded lookup for the log
+    pub bounded_log_lookup: bool,
 }
 
 impl Default for RunArgs {
     fn default() -> Self {
         Self {
+            bounded_log_lookup: false,
             tolerance: Tolerance::default(),
             input_scale: 7,
             param_scale: 7,
@@ -333,7 +350,6 @@ impl Default for RunArgs {
             input_visibility: Visibility::Private,
             output_visibility: Visibility::Public,
             param_visibility: Visibility::Private,
-            div_rebasing: false,
             rebase_frac_zero_constants: false,
             check_mode: CheckMode::UNSAFE,
             commitment: None,

src/pfsys/mod.rs

@@ -59,10 +59,7 @@ fn serde_format_from_str(s: &str) -> halo2_proofs::SerdeFormat {
 
 #[allow(missing_docs)]
 #[derive(Copy, Clone, Default, Debug, PartialEq, Eq, Deserialize, Serialize, PartialOrd)]
-#[cfg_attr(
-    all(feature = "ezkl", not(target_arch = "wasm32")),
-    derive(ValueEnum)
-)]
+#[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), derive(ValueEnum))]
 pub enum ProofType {
     #[default]
     Single,
@@ -134,10 +131,7 @@ impl<'source> pyo3::FromPyObject<'source> for ProofType {
 
 #[allow(missing_docs)]
 #[derive(Copy, Clone, Debug, PartialEq, Eq, Deserialize, Serialize)]
-#[cfg_attr(
-    all(feature = "ezkl", not(target_arch = "wasm32")),
-    derive(ValueEnum)
-)]
+#[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), derive(ValueEnum))]
 pub enum StrategyType {
     Single,
     Accum,
@@ -203,10 +197,7 @@ pub enum PfSysError {
 
 #[allow(missing_docs)]
 #[derive(Default, Copy, Clone, Debug, PartialEq, Eq, Deserialize, Serialize, PartialOrd)]
-#[cfg_attr(
-    all(feature = "ezkl", not(target_arch = "wasm32")),
-    derive(ValueEnum)
-)]
+#[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), derive(ValueEnum))]
 pub enum TranscriptType {
     Poseidon,
     #[default]
@@ -324,6 +315,8 @@ where
     pub timestamp: Option<u128>,
     /// commitment
     pub commitment: Option<Commitments>,
+    /// (optional) version of ezkl used to generate the proof
+    version: Option<String>,
 }
 
 #[cfg(feature = "python-bindings")]
@@ -385,6 +378,7 @@ where
                     .as_millis(),
             ),
             commitment,
+            version: Some(crate::version().to_string()),
         }
     }
 
@@ -920,6 +914,7 @@ mod tests {
             pretty_public_inputs: None,
             timestamp: None,
             commitment: None,
+            version: None,
         };
 
         snark

src/tensor/ops.rs

@@ -1474,6 +1474,93 @@ pub mod nonlinearities {
         .unwrap()
     }
 
+    /// Checks if a tensor's elements are odd
+    /// # Arguments
+    /// * `a` - Tensor
+    /// * `scale` - Single value
+    /// # Examples
+    /// ```
+    /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
+    /// use ezkl::tensor::ops::nonlinearities::is_odd;
+    /// let x = Tensor::<IntegerRep>::new(
+    ///   Some(&[2, 15, 2, 1, 1, 0]),
+    /// &[2, 3],
+    /// ).unwrap();
+    ///
+    /// let result = is_odd(&x);
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[0, 1, 0, 1, 1, 0]), &[2, 3]).unwrap();
+    /// assert_eq!(result, expected);
+    /// ```
+    pub fn is_odd(a: &Tensor<IntegerRep>) -> Tensor<IntegerRep> {
+        a.par_enum_map(|_, a_i| {
+            let rounded = if a_i % 2 == 0 { 0 } else { 1 };
+            Ok::<_, TensorError>(rounded)
+        })
+        .unwrap()
+    }
+
+    /// Powers of 2
+    /// # Arguments
+    /// * `a` - Tensor
+    /// * `scale` - Single value
+    /// # Examples
+    /// ```
+    /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
+    /// use ezkl::tensor::ops::nonlinearities::ipow2;
+    /// let x = Tensor::<IntegerRep>::new(
+    ///  Some(&[2, 15, 2, 1, 1, 0]),
+    /// &[2, 3],
+    /// ).unwrap();
+    /// let result = ipow2(&x, 1.0);
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[4, 32768, 4, 2, 2, 1]), &[2, 3]).unwrap();
+    /// assert_eq!(result, expected);
+    /// ```
+    pub fn ipow2(a: &Tensor<IntegerRep>, scale_output: f64) -> Tensor<IntegerRep> {
+        a.par_enum_map(|_, a_i| {
+            let kix = a_i as f64;
+            let kix = scale_output * (2.0_f64).powf(kix);
+            let rounded = kix.round();
+            Ok::<_, TensorError>(rounded as IntegerRep)
+        })
+        .unwrap()
+    }
+
+    /// Elementwise applies ln base 2 to a tensor of integers.
+    /// # Arguments
+    /// * `a` - Tensor
+    /// * `scale_input` - Single value
+    /// ```
+    /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
+    /// use ezkl::tensor::ops::nonlinearities::ilog2;
+    /// let x = Tensor::<IntegerRep>::new(
+    ///    Some(&[2, 15, 2, 1, 1, 2]),
+    /// &[2, 3],
+    /// ).unwrap();
+    /// let result = ilog2(&x, 1.0);
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[1, 4, 1, 0, 0, 1]), &[2, 3]).unwrap();
+    /// assert_eq!(result, expected);
+    /// ```
+    pub fn ilog2(a: &Tensor<IntegerRep>, scale_input: f64) -> Tensor<IntegerRep> {
+        a.par_enum_map(|_, a_i| {
+            let kix = (a_i as f64) / scale_input;
+            let log = (kix).log2();
+            let floor = log.floor();
+            let ceil = log.ceil();
+            let floor_dist = ((2.0_f64).powf(floor) - kix).abs();
+            let ceil_dist = (kix - (2.0_f64).powf(ceil)).abs();
+
+            if floor_dist < ceil_dist {
+                Ok::<_, TensorError>(floor as IntegerRep)
+            } else {
+                Ok::<_, TensorError>(ceil as IntegerRep)
+            }
+        })
+        .unwrap()
+    }
+
     /// Elementwise applies sigmoid to a tensor of integers.
     /// # Arguments
     ///
@@ -1577,7 +1664,7 @@ pub mod nonlinearities {
     ///     Some(&[2, 15, 2, 1, 1, 0]),
     ///     &[2, 3],
     /// ).unwrap();
-    /// let result = exp(&x, 1.0);
+    /// let result = exp(&x, 1.0,  std::f64::consts::E);
     /// let expected = Tensor::<IntegerRep>::new(Some(&[7, 3269017, 7, 3, 3, 1]), &[2, 3]).unwrap();
     /// assert_eq!(result, expected);
     ///
@@ -1586,28 +1673,27 @@ pub mod nonlinearities {
     ///    Some(&[37, 12, 41]),
     ///   &[3],
     /// ).unwrap();
-    /// let result = exp(&x, 512.0);
+    /// let result = exp(&x, 512.0, std::f64::consts::E);
     ///
     /// let expected = Tensor::<IntegerRep>::new(Some(&[550, 524, 555]), &[3]).unwrap();
     ///
     /// assert_eq!(result, expected);
     /// ```
-    pub fn exp(a: &Tensor<IntegerRep>, scale_input: f64) -> Tensor<IntegerRep> {
+    pub fn exp(a: &Tensor<IntegerRep>, scale_input: f64, base: f64) -> Tensor<IntegerRep> {
         a.par_enum_map(|_, a_i| {
             let kix = (a_i as f64) / scale_input;
-            let fout = scale_input * kix.exp();
+            let fout = scale_input * base.powf(kix);
             let rounded = fout.round();
             Ok::<_, TensorError>(rounded as IntegerRep)
         })
         .unwrap()
     }
 
-    /// Elementwise applies exponential to a tensor of integers.
+    /// Elementwise applies ln to a tensor of integers.
     /// # Arguments
     ///
     /// * `a` - Tensor
     /// * `scale_input` - Single value
-    /// * `scale_output` - Single value
     /// # Examples
     /// ```
     /// use ezkl::tensor::Tensor;

tests/assets/settings.json

@@ -27,7 +27,8 @@
         "check_mode": "UNSAFE",
         "commitment": "KZG",
         "decomp_base": 128,
-        "decomp_legs": 2
+        "decomp_legs": 2,
+        "bounded_log_lookup": false
     },
     "num_rows": 46,
     "total_assignments": 92,

tests/assets/witness.json

@@ -1 +1 @@
-{"inputs":[["0200000000000000000000000000000000000000000000000000000000000000","0100000000000000000000000000000000000000000000000000000000000000","0100000000000000000000000000000000000000000000000000000000000000"]],"pretty_elements":{"rescaled_inputs":[["2","1","1"]],"inputs":[["0x0000000000000000000000000000000000000000000000000000000000000002","0x0000000000000000000000000000000000000000000000000000000000000001","0x0000000000000000000000000000000000000000000000000000000000000001"]],"processed_inputs":[],"processed_params":[],"processed_outputs":[],"rescaled_outputs":[["0","0","0","0"]],"outputs":[["0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000"]]},"outputs":[["0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000"]],"processed_inputs":null,"processed_params":null,"processed_outputs":null,"max_lookup_inputs":0,"min_lookup_inputs":0,"max_range_size":127}
+{"inputs":[["0200000000000000000000000000000000000000000000000000000000000000","0100000000000000000000000000000000000000000000000000000000000000","0100000000000000000000000000000000000000000000000000000000000000"]],"pretty_elements":{"rescaled_inputs":[["2","1","1"]],"inputs":[["0x0000000000000000000000000000000000000000000000000000000000000002","0x0000000000000000000000000000000000000000000000000000000000000001","0x0000000000000000000000000000000000000000000000000000000000000001"]],"processed_inputs":[],"processed_params":[],"processed_outputs":[],"rescaled_outputs":[["0","0","0","0"]],"outputs":[["0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000"]]},"outputs":[["0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000"]],"processed_inputs":null,"processed_params":null,"processed_outputs":null,"max_lookup_inputs":0,"min_lookup_inputs":0,"max_range_size":127,"version":"source - no compatibility guaranteed"}

tests/integration_tests.rs

@@ -205,7 +205,7 @@ mod native_tests {
         "1l_tiny_div",
     ];
 
-    const TESTS: [&str; 95] = [
+    const TESTS: [&str; 98] = [
         "1l_mlp", //0
         "1l_slice",
         "1l_concat",
@@ -305,6 +305,9 @@ mod native_tests {
         "lstm_medium", // 92
         "lenet_5",     // 93
         "rsqrt",       // 94
+        "log",         // 95
+        "exp",         // 96
+        "general_exp", // 97
     ];
 
     const WASM_TESTS: [&str; 46] = [
@@ -489,7 +492,7 @@ mod native_tests {
             #[cfg(feature="icicle")]
             seq!(N in 0..=2 {
             #(#[test_case(TESTS_AGGR[N])])*
-            fn aggr_prove_and_verify_(test: &str) {
+            fn kzg_aggr_prove_and_verify_(test: &str) {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(test_dir.path().to_str().unwrap(), test);
@@ -538,12 +541,12 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "public", "fixed", "public", 1, "accuracy", None, 0.0);
+                mock(path, test.to_string(), "public", "fixed", "public", 1, "accuracy", None, 0.0, false);
                 test_dir.close().unwrap();
             }
         });
 
-            seq!(N in 0..=94 {
+            seq!(N in 0..=97 {
 
             #(#[test_case(TESTS[N])])*
             #[ignore]
@@ -555,15 +558,7 @@ mod native_tests {
                 test_dir.close().unwrap();
             }
 
-            #(#[test_case(TESTS[N])])*
-            fn accuracy_measurement_div_rebase_(test: &str) {
-                crate::native_tests::init_binary();
-                crate::native_tests::setup_py_env();
-                let test_dir = TempDir::new(test).unwrap();
-                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                accuracy_measurement(path, test.to_string(), "private", "private", "public", 1, "accuracy", 2.6, true);
-                test_dir.close().unwrap();
-            }
+
 
             #(#[test_case(TESTS[N])])*
             fn accuracy_measurement_public_outputs_(test: &str) {
@@ -571,7 +566,7 @@ mod native_tests {
                 crate::native_tests::setup_py_env();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                accuracy_measurement(path, test.to_string(), "private", "private", "public", 1, "accuracy", 2.6, false);
+                accuracy_measurement(path, test.to_string(), "private", "private", "public", 1, "accuracy", 2.6);
                 test_dir.close().unwrap();
             }
 
@@ -581,7 +576,7 @@ mod native_tests {
                 crate::native_tests::setup_py_env();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                accuracy_measurement(path, test.to_string(), "private", "fixed", "private", 1, "accuracy", 2.6 , false);
+                accuracy_measurement(path, test.to_string(), "private", "fixed", "private", 1, "accuracy", 2.6 );
                 test_dir.close().unwrap();
             }
 
@@ -591,7 +586,7 @@ mod native_tests {
                 crate::native_tests::setup_py_env();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                accuracy_measurement(path, test.to_string(), "public", "private", "private", 1, "accuracy", 2.6, false);
+                accuracy_measurement(path, test.to_string(), "public", "private", "private", 1, "accuracy", 2.6);
                 test_dir.close().unwrap();
             }
 
@@ -602,7 +597,7 @@ mod native_tests {
                 crate::native_tests::setup_py_env();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                accuracy_measurement(path, test.to_string(), "private", "private", "public", 1, "resources", 3.1, false);
+                accuracy_measurement(path, test.to_string(), "private", "private", "public", 1, "resources", 3.1);
                 test_dir.close().unwrap();
             }
 
@@ -611,7 +606,17 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, 0.0, false);
+                test_dir.close().unwrap();
+            }
+
+
+            #(#[test_case(TESTS[N])])*
+            fn mock_bounded_lookup_log(test: &str) {
+                crate::native_tests::init_binary();
+                let test_dir = TempDir::new(test).unwrap();
+                let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
+                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, 0.0, true);
                 test_dir.close().unwrap();
             }
 
@@ -622,7 +627,7 @@ mod native_tests {
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
                 // gen random number between 0.0 and 1.0
                 let tolerance = rand::thread_rng().gen_range(0.0..1.0) * 100.0;
-                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, tolerance);
+                mock(path, test.to_string(), "private", "private", "public", 1, "resources", None, tolerance, false);
                 test_dir.close().unwrap();
             }
 
@@ -637,7 +642,7 @@ mod native_tests {
                     let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
                     let large_batch_dir = &format!("large_batches_{}", test);
                     crate::native_tests::mk_data_batches_(path, test, &large_batch_dir, 10);
-                    mock(path, large_batch_dir.to_string(), "private", "private", "public", 10, "resources", None, 0.0);
+                    mock(path, large_batch_dir.to_string(), "private", "private", "public", 10, "resources", None, 0.0, false);
                     test_dir.close().unwrap();
                 }
             }
@@ -647,7 +652,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "public", "private", "private", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "public", "private", "private", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -656,7 +661,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "public", "hashed", "private", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "public", "hashed", "private", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -665,7 +670,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "fixed", "private", "private", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "fixed", "private", "private", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -674,7 +679,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "private", "private", "fixed", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "private", "private", "fixed", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -683,7 +688,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "private", "fixed", "private", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "private", "fixed", "private", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -692,7 +697,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "hashed", "private", "public", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "hashed", "private", "public", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -701,7 +706,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "polycommit", "private", "public", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "polycommit", "private", "public", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -711,7 +716,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "private", "hashed", "public", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "private", "hashed", "public", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -721,7 +726,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "private", "polycommit", "public", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "private", "polycommit", "public", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -730,7 +735,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "public", "private", "hashed", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "public", "private", "hashed", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -740,7 +745,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "public", "private", "polycommit", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "public", "private", "polycommit", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -749,7 +754,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "public", "fixed", "hashed", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "public", "fixed", "hashed", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -759,7 +764,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "public", "polycommit", "hashed", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "public", "polycommit", "hashed", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -769,7 +774,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "polycommit", "polycommit", "polycommit", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "polycommit", "polycommit", "polycommit", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -779,7 +784,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "hashed", "private", "hashed", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "hashed", "private", "hashed", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -789,7 +794,7 @@ mod native_tests {
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
                 // needs an extra row for the large model
-                mock(path, test.to_string(),"hashed", "hashed", "public", 1, "resources", None, 0.0);
+                mock(path, test.to_string(),"hashed", "hashed", "public", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -799,7 +804,7 @@ mod native_tests {
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
                 // needs an extra row for the large model
-                mock(path, test.to_string(),"hashed", "hashed", "hashed", 1, "resources", None, 0.0);
+                mock(path, test.to_string(),"hashed", "hashed", "hashed", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
 
@@ -976,7 +981,7 @@ mod native_tests {
                 crate::native_tests::init_binary();
                 let test_dir = TempDir::new(test).unwrap();
                 let path = test_dir.path().to_str().unwrap(); crate::native_tests::mv_test_(path, test);
-                mock(path, test.to_string(), "private", "fixed", "public", 1, "resources", None, 0.0);
+                mock(path, test.to_string(), "private", "fixed", "public", 1, "resources", None, 0.0, false);
                 test_dir.close().unwrap();
             }
         });
@@ -1453,6 +1458,7 @@ mod native_tests {
         cal_target: &str,
         scales_to_use: Option<Vec<u32>>,
         tolerance: f32,
+        bounded_lookup_log: bool,
     ) {
         let mut tolerance = tolerance;
         gen_circuit_settings_and_witness(
@@ -1465,10 +1471,10 @@ mod native_tests {
             cal_target,
             scales_to_use,
             2,
-            false,
             &mut tolerance,
             Commitments::KZG,
             2,
+            bounded_lookup_log,
         );
 
         if tolerance > 0.0 {
@@ -1606,10 +1612,10 @@ mod native_tests {
         cal_target: &str,
         scales_to_use: Option<Vec<u32>>,
         num_inner_columns: usize,
-        div_rebasing: bool,
         tolerance: &mut f32,
         commitment: Commitments,
         lookup_safety_margin: usize,
+        bounded_lookup_log: bool,
     ) {
         let mut args = vec![
             "gen-settings".to_string(),
@@ -1628,9 +1634,9 @@ mod native_tests {
             format!("--commitment={}", commitment),
         ];
 
-        if div_rebasing {
-            args.push("--div-rebasing".to_string());
-        };
+        if bounded_lookup_log {
+            args.push("--bounded-log-lookup".to_string());
+        }
 
         let status = Command::new(format!("{}/release/ezkl", *CARGO_TARGET_DIR))
             .args(args)
@@ -1730,7 +1736,6 @@ mod native_tests {
         batch_size: usize,
         cal_target: &str,
         target_perc: f32,
-        div_rebasing: bool,
     ) {
         gen_circuit_settings_and_witness(
             test_dir,
@@ -1742,10 +1747,10 @@ mod native_tests {
             cal_target,
             None,
             2,
-            div_rebasing,
             &mut 0.0,
             Commitments::KZG,
             2,
+            false,
         );
 
         println!(
@@ -2026,10 +2031,10 @@ mod native_tests {
             target_str,
             scales_to_use,
             num_inner_columns,
-            false,
             &mut 0.0,
             commitment,
             lookup_safety_margin,
+            false,
         );
 
         let settings_path = format!("{}/{}/settings.json", test_dir, example_name);
@@ -2458,10 +2463,10 @@ mod native_tests {
             // we need the accuracy
             Some(vec![4]),
             1,
-            false,
             &mut 0.0,
             Commitments::KZG,
             2,
+            false,
         );
 
         let model_path = format!("{}/{}/network.compiled", test_dir, example_name);

tests/py_integration_tests.rs

@@ -76,8 +76,8 @@ mod py_tests {
                     "nbconvert==7.16.3",
                     "onnx==1.16.0",
                     "kaggle==1.6.8",
-                    "py-solc-x==2.0.2",
-                    "web3==6.16.0",
+                    "py-solc-x==2.0.3",
+                    "web3==7.5.0",
                     "librosa==0.10.1",
                     "keras==3.1.1",
                     "tensorflow==2.16.1",
@@ -123,7 +123,7 @@ mod py_tests {
         }
     }
 
-    const TESTS: [&str; 34] = [
+    const TESTS: [&str; 35] = [
         "ezkl_demo_batch.ipynb",                   // 0
         "proof_splitting.ipynb",                   // 1
         "variance.ipynb",                          // 2
@@ -158,6 +158,7 @@ mod py_tests {
         "mnist_classifier.ipynb",                  // 31
         "world_rotation.ipynb",                    // 32
         "logistic_regression.ipynb",               // 33
+        "univ3-da.ipynb",                          // 34
     ];
 
     macro_rules! test_func {